[1]

[2]

# Machine Learning and Vision Transformers for Thyroid Carcinoma Diagnosis: A
review

Yassine Habchi<EMAIL_ADDRESS>Hamza Kheddar<EMAIL_ADDRESS>Yassine Himeur  Abdelkrim Boukabou  Ammar Chouchane  Abdelmalik Ouamane  Shadi
Atalla  Wathiq Mansoor  Institute of Technology, University Center Salhi
Ahmed, Naama, Algeria  LSEA Laboratory, Electrical Engineering Department,
University of Medea, 26000, Algeria  College of Engineering and Information
Technology, University of Dubai, Dubai, UAE  Department of Electronics,
University of Jijel, BP 98 Ouled Aissa, 18000 Jijel, Algeria  University
Center of Barika. Amdoukal Road, Barika, 05001, Algeria.  Laboratory of LI3C,
Mohamed Khider University, Biskra, Algeria

###### Abstract

The growing interest in developing smart diagnostic systems to help medical
experts process extensive data for treating incurable diseases has been
notable. In particular, the challenge of identifying thyroid cancer (TC) has
seen progress with the use of machine learning (ML) and big data analysis,
incorporating transformers to evaluate TC prognosis and determine the risk of
malignancy in individuals. This review article presents a summary of various
studies on AI-based approaches, especially those employing transformers, for
diagnosing TC. It introduces a new categorization system for these methods
based on artificial intelligence (AI) algorithms, the goals of the framework,
and the computing environments used. Additionally, it scrutinizes and
contrasts the available TC datasets by their features. The paper highlights
the importance of AI instruments in aiding the diagnosis and treatment of TC
through supervised, unsupervised, or mixed approaches, with a special focus on
the ongoing importance of transformers in medical diagnostics and disease
management. It further discusses the progress made and the continuing
obstacles in this area. Lastly, it explores future directions and focuses
within this research field.

###### keywords:

Thyroid carcinoma detection Artificial intelligence Deep learning Biomerdical
imagesTransformers

##  1 Introduction

AC

    active contour
AI

    artificial intelligence
ANN

    artificial neural network
ATC

    anaplastic thyroid carcinoma
BA

    bootstrap aggregation
Bi-LSTM

    bi-directional LSTM
BN

    bayesian network
CAD

    computer-aided diagnosis
CFS

    correlation-based feature selection
CNN

    convolutional neural network
CT

    computed tomography
DAE

    denoising autoencoder
DCG

    discounted cumulative gain
DCNN

    deep convolutional neural network
DDTI

    digital database thyroind image
DL

    deep learning
DTL

    deep transfer learning
DRL

    deep reinforcement learning
DNN

    deep neural network
DR

    dimensionality reduction
DT

    decision trees
DTCW

    double-tree complex wavelet transform
DWT

    discrete wavelet transform
ELM

    extreme learning machine
ER

    error rate
FB

    feature bagging
FL

    federated learning
FNAB

    fine-needle aspiration biopsy
FTC

    follicular thyroid carcinoma
GAN

    generative adversarial network
GEO

    gene expression omnibus
GLCM

    gray-level co-occurrence matrix
HOG

    histogram of oriented gradient
ICA

    independent component analysis
IG

    information gain
IoMIT

    internet of medical imaging thing
KM

    K-means
KNN

    k-nearest neighbors
LBP

    local binary patterns
LR

    logistic regression
LSTM

    long-short-term-memory
ML

    machine learning
MLP

    multilayer perceptron
MRI

    magnetic resonance imaging
MRM

    microRNA regulatory module
MRR

    mean reciprocal rank
MSE

    mean squared error
MTC

    medullary thyroid carcinoma
NCDR

    national cancer data repository
PCA

    principal component analysis
PLCO

    prostate, lung, colorectal, and ovarian
PM

    probabilistic models
PS

    panoptic segmentation
PSNR

    peak signal to noise ratio
PTC

    papillary carcinoma
RBF

    radial basis function
RBM

    restricted Boltzmann machine
RF

    random forest
RL

    reinforcement learning
RMSE

    root mean square error
RNN

    recurrent neural network
RS

    recommender systems
SEER

    surveillance, epidemiology, and end results
SL

    supervised learning
SVM

    support vector machine
TC

    thyroid cancer
TCD

    thyroid cancer dataset
TCGA

    cancer genome atlas
TCL

    traditional classification
TD

    thyroid disease
TDDS

    thyroid disease data set
TG

    thyroid gland
TIRADS

    thyroid imaging reporting and data system
TI-RADS

    thyroid imaging, reporting, and data system
TL

    transfer learning
TN

    Thyroid nodules
US

    ultrasound
USL

    unsupervised learning
XAI

    explainable AI
XAI

    explainable artificial intelligence
XGBoost

    Gradient tree boosting
NLP

    natural language processing
BERT

    bidirectional encoder representations from transformers
ROC

    receiver operating characteristic
---

The integration of AI into the healthcare sector represents a pivotal
advancement, fundamentally altering the landscape of medical diagnostics,
therapy, and patient management. The superior capabilities of AI,
Incorporating the identification of patterns, forecasting analytics, and the
process of making decisions, have led to the creation of systems that can
interpret intricate medical data with greater accuracy and scale than ever
before [1, 2]. Such advancements facilitate the early identification of
diseases, enhance the accuracy of diagnoses, and support the customization of
treatment plans for individuals. In addition, predictive models powered by AI
are capable of foreseeing disease spread, boosting the efficiency of
healthcare operations, and significantly improving outcomes for patients [3].
AI also has the potential to make healthcare more equitable by reducing
disparities in service quality between rural and urban areas, thus improving
access to premium healthcare services. As a result, the role of AI is
significant in healthcare and is anticipated to grow as ongoing technological
innovations lead to the creation of even more advanced applications, promising
widespread benefits for patient health across the globe [4, 5].

Nonetheless, trust serves as a critical intermediary, impacting the extent to
which factors related to AI affect user acceptance. Research has explored the
roles of trust, risk, and security in determining the uptake of AI-powered
support [6]. Empirical investigations within these studies have underscored
the essential influence of trust in forming the basis of user acceptance.

Cancer is marked by the unchecked growth of cells across various parts of the
body. These cells multiply erratically and can spread, damaging healthy tissue
[7]. Such uncontrolled cell growth is triggered by changes or mutations in the
DNA of these cells [8]. The DNA in cells comprises multiple genes, which
provide the instructions necessary for a cell’s function, growth, and
division. When these instructions are erroneous, it can interrupt the normal
functioning of cells and may result in the development of cancer [9, 10, 11,
12]. thyroid cancer (TC), in particular, is recognized as one of the most
common types of endocrine malignancies around the globe [13, 14].

Recent global epidemiological studies indicate a rise in abnormal thyroid
nodules, linked to an upsurge in genetic cellular activity. This suggests an
increase in normal cell functions, with anomalies classified into four primary
types: follicular thyroid carcinoma (FTC), papillary carcinoma (PTC),
medullary thyroid carcinoma (MTC), and anaplastic thyroid carcinoma (ATC) [15,
16, 17, 18]. Elements like exposure to radiation, Hashimoto’s thyroiditis,
psychological factors, and genetic components, alongside advances in
technologies of of detection, appear these cancers. These factors can cause
chronic health issues like diabetes and blood pressure instability. The cell
cancer volume is a key to evaluating the aggressiveness and prognosis of TC’s,
with cell nuclei detection offering alternative markers for evaluating cancer
cell proliferation. CAD systems have gained prominence in TC analysis,
improving diagnostic accuracy and reducing interpretation times [19, 20].
Radiomics, particularly through ultrasound (US) imaging [21], has emerged as
an efficient diagnostic method. The American College of Radiology’s thyroid
imaging reporting and data system (TIRADS) categorizes thyroid nodules from
benign to malignant [22, 23, 24]. Despite available open-source tools for
nodule analysis, accurately identifying them remains a challenge, reliant on
radiologists’ experience and the subjective nature of visual image analysis
[25].

Additionally, US imaging can be a lengthy and stress-inducing process, which
may result in incorrect diagnoses. It is common to encounter classification
errors among cases deemed normal, benign, malignant, or of uncertain nature
[26, 27, 28, 29, 30, 31]. For a more precise diagnosis, a fine-needle
aspiration biopsy (FNAB) is often conducted. Yet, this technique can be
uncomfortable for patients, and inaccuracies by the practitioner can
mistakenly label benign nodules as malignant, leading to unnecessary costs
[32, 33]. The main issue is the selection of nodule characteristics critical
for accurately differentiating between benign and malignant cancer. Various
research efforts have delved into the use of conventional US imaging to
characterize different types of cancers, such as retinal [34, 35], breast [36,
37, 38, 39, 40], blood [41, 42], and TCs [43, 44]. Despite these efforts,
there remains a lack of accuracy in the methods available for effectively
categorizing thyroid nodules (see Figure 1).

Figure 1: Approaches for identifying TC.

The deployment of AI technology is crucial in diminishing subjectivity and
boosting the precision of pathological assessments, particularly for complex
conditions like thyroid diseases [45, 46]. These advancements enhance the the
analysis of images obtained through US and expedite analysis times. ML and
deep learning (DL) stand out as effective strategies for automating the
differentiation of TNs in various contexts, including US, fine-needle
aspiration (FNA), and during thyroid surgical procedures [47, 48]. The
efficacy of these methodologies has been underscored in multiple research
studies, as evidenced by [49, 50, 51, 46, 52, 53].

Current research efforts are delving into the utilization of innovative
technologies for cancer detection, where the success of these methods depends
on the amount of data accessible and the accuracy of the classification
strategies. The drive to compile a review on ”AI in the Diagnosis of TC” is
motivated by the rising rates of TC, a critical concern in endocrinology where
early and precise diagnosis significantly impacts patient outcomes. As AI and
ML continue to evolve, their incorporation into various medical diagnostic
fields, including imaging, pathology, and genomics, promises to enhance the
accuracy and speed of detection processes. Traditional methods for diagnosing
TC, like fine-needle aspiration biopsies, can often produce ambiguous
outcomes, whereas AI presents an opportunity for more accurate and less
invasive alternatives. This review seeks to integrate insights from pathology,
computer science, ocrinologyand, and radiology, encouraging cross-disciplinary
collaboration. It will also explore the clinical significance of AI, offering
recommendations for healthcare professionals on utilizing AI advancements for
improving patient care, and pinpointing directions for future research
endeavors. Additionally, the review will discuss the healthcare and economic
system benefits, including cost savings and reduced wait times. Yet, it’s
essential to confront the challenges AI brings, such as ethical considerations
and data privacy, to facilitate its responsible integration into healthcare
practices. This review aims to provide an extensive examination of AI’s
current and future impact on the detection of TC, serving as a resource for
both researchers and clinicians.

###  1.1 Contribution of the paper

This review explores the use of AI in identifying TC, emphasizing the shift
towards improved diagnostic accuracy using AI techniques in healthcare,
specifically for detecting TC. It begins with an overview of current
frameworks and delves into AI strategies such as DL, artificial neural
networks, vision Transformers, conventional classification, predictive
modeling, unsupervised learning (USL) like clustering, and ensemble methods
like boosting and bagging. The significance of comprehensive datasets for AI
success is discussed, alongside an analysis of TC datasets, feature selection,
and extraction methods. It evaluates AI effectiveness in thyroid cancer
dataset (TCD) through various metrics and concludes by highlighting future
research directions to overcome challenges and enhance AI deployment in TCD.
The review underscores AI’s potential to revolutionize TCD, advocating for
ongoing assessment to ensure ethical and effective use.

The main advancements presented in our paper are:

  * •

A review of current frameworks coupled with a detailed investigation into
diverse AI strategies, including supervised learning, conventional
classification, DL, Transformers, probabilistic models (PM), and USL
techniques. Additionally, ensemble approaches like bagging and boosting are
scrutinized.

  * •

An in-depth review of various TCDs, detailing their attributes and examining
methods for feature selection and extraction used in different studies

  * •

A detailed discussion on the benchmark criteria for assessing the efficacy of
AI-powered approaches in identifying TC. These evaluation metrics cover a wide
range, from regression and classification parameters to statistical, computer
vision, and ranking parameters.

  * •

A thorough critique and exploration of the challenges, limitations, prevalent
trends, and unresolved questions in the domain.

  * •

An analysis of future research priorities, highlighting specific areas that
require more investigation to address current challenges and improve methods
for detecting TC.

  * •

A spotlight on the transformative impact of AI in enhancing TC diagnosis,
stressing the importance of continual critical review to guarantee its ethical
and effective application.

Additionally, the main contributions of this review, as differentiated from
other reviews, are summarized in Table 1.

Table 1: The notable advancements made by the suggested review in the
categorization of TC when contrasted with similar research endeavors.

Ref |  Year |  Patient |  TC detec. |  AI |  ML |  DL |  Trans |  Feat |  TCE | Prospective path |  Metric
---|---|---|---|---|---|---|---|---|---|---|---
|  |  privacy |  schemes |  apps |  |  |  |  |  |  IoMIT |  RS |  RL |  PS |  XAI |  EFC-AI |
[54] |  2021 |  Yes |  Yes |  No |  No |  Yes |  No |  Yes |  No |  No |  No |  No |  No |  No |  No |  No
[55] |  2021 |  Yes |  Yes |  No |  No |  Yes |  No |  No |  No |  No |  No |  No |  No |  No |  No |  No
[56] |  2021 |  Yes |  Yes |  No |  Yes |  Yes |  No |  Yes |  Yes |  No |  No |  No |  No |  No |  Yes |  Yes
[57] |  2021 |  Yes |  Yes |  No |  No |  Yes |  No |  No |  No |  No |  No |  No |  No |  No |  Yes |  Yes
[58] |  2021 |  Yes |  Yes |  No |  Yes |  Yes |  No |  No |  No |  No |  No |  No |  No |  No |  Yes |  Yes
[59] |  2022 |  Yes |  Yes |  No |  No |  Yes |  No |  No |  No |  No |  No |  No |  No |  No |  No |  Yes
[60] |  2022 |  Yes |  Yes |  No |  No |  Yes |  No |  No |  No |  No |  No |  No |  No |  Yes |  Yes |  No
[61] |  2022 |  Yes |  Yes |  No |  Yes |  Yes |  No |  Yes |  No |  No |  No |  No |  No |  No |  Yes |  Yes
[62] |  2022 |  Yes |  Yes |  No |  Yes |  Yes |  No |  No |  No |  No |  No |  No |  No |  No |  Yes |  Yes
Our |  - |  Yes |  Yes |  Yes |  Yes |  Yes |  Yes |  Yes |  Yes |  Yes |  Yes |  Yes |  Yes |  Yes |  Yes |  Yes

Abbreviations: Artificial intelligence applications (AI apps), Machine
learning (ML), Deep learning (DL), Transformers (Trans), Features (Feat), TC
example (TCE), Internet of medical imaging thing (IoMT), Recommender systems
(RS), Reinforcement learning (RL), Panoptic segmentation (PS), Explainable
artificial intelligence (XAI), Edge, fog and cloud networks based on AI (EFC-
AI).

###  1.2 Bibliometric analysis

A bibliometric analysis was performed to delve into and evaluate the
scientific studies reviewed in this paper. The continuous interest in AI-based
TC research is depicted in Figure 2, with the publication count reaching 81 in
2022. Figure 2 (a) highlights the leading researchers in the field of TC-
oriented AI research, focusing on those who have published within the past
five years. Figure 2 (b) provides a snapshot of the enduring interest in AI-
based TC research, showcasing a rising trend in the creation of AI solutions
for TC since 2015. Figure 2 (c) maps out the countries that are major
contributors to the research output in this area, with China and the United
States showing a pronounced focus on AI-driven TC detection. Lastly, Figure 2
(d) illustrates the breakdown of publication types, with journal articles
making up the bulk of the research (67.2%), followed by conference papers
(19.3%).

Figure 2: Bibliometric analysis in terms of (a). Documents by author; (b).
Documents by year; (c). Documents by country; (d). Documents by type.

###  1.3 Roadmap

The subsequent sections of this manuscript are organized as follows: Section 2
offers a detailed analysis of the frameworks currently employed in this field,
outlining their advantages and disadvantages. In Section 3.2, various TCDs
employed in AI-driven analyses are introduced, offering insights into their
pertinence and distinctiveness. Section 4 focuses on a pivotal aspect,
’Features,’ detailing the techniques utilized for feature extraction and
selection in AI models tailored for TCD. Following this, Section 3 presents a
synopsis of contemporary methodologies for TCD employing DL and transformers,
alongside an evaluation of their efficacy.

The ensuing part of our document delves into a meticulous critique and
dialogue in Section 5.2, meticulously evaluating the obstacles, deficiencies,
and segments requiring advancements within existing methodologies. Proceeding
to Section 6, it delineates prospective trajectories for forthcoming research,
underlining areas ripe for deeper exploration and innovation that could propel
forward the efficacy of AI in diagnosing TC. The discourse culminates in
Section 7, encapsulating the core insights and deliberations, thereby
rendering a holistic conclusion to the subjects broached in earlier sections.
This structured approach not only showcases our commitment to addressing the
nuanced complexities of AI application in medical diagnostics but also signals
our dedication to fostering a forward-looking perspective on how these
challenges can be navigated to harness the full potential of AI technologies
in enhancing TCD.

##  2 Summary of current models and methods

In this section, we discuss the various AI-based methodologies utilized for
diagnosing thyroid gland (TG) cancers. Figure 3 offers a visual depiction of
the classification system we propose for methods of TC diagnosis leveraging
AI.

Figure 3: Classification of TCD strategies utilizing AI.

###  2.1 Pre-processing

dimensionality reduction (DR) is a process applied in ML, mainly during the
preprocessing and feature crafting stages, aimed at converting data from a
high-dimensional space into a more manageable lower-dimensional space. This
technique is valued for its efficiency and its capacity to reduce unnecessary
data patterns while decreasing redundancy, serving multiple objectives. For
example, DR was utilized in the diagnosis of thyroid disease (TD) through the
analysis of cytological images [63].

PCA operates as a sophisticated method for preprocessing, transforming samples
(variables) into a smaller set of uncorrelated ones. This technique
effectively reduces the volume of variables, thus cutting down on redundant
data, all the while striving to maintain the integrity of the data
relationships. principal component analysis (PCA) is extensively used in the
realms of cancer detection and distinguishing between malignant and benign
thyroid cells. In research conducted by Shankarlal et al. [64], PCA was
deployed to filter the most relevant set of wavelet coefficients from double-
tree complex wavelet transform (DTCW) processed noisy thyroid images, which
were then categorized using random forest (RF). In another instance,
Soulaymani et al. [65] applied PCA to a dataset comprising 399 patients with
three different TC types (undifferentiated, follicular, and papillary ),
allowing the classification based on variables like age, sex, type of cancer,
and geographical location.

###  2.2 Purpose of AI-driven examination

This research centers on the use of AI for identifying TC. Comprehending the
foundational objectives of each framework is essential for acquiring a more
profound understanding of their reasoning [8]. (a) Classification of thyroid
carcinoma: Entails the sorting of TCs according to their histopathological
features, clinical manifestations, and prognostic outcomes. Various forms of
thyroid carcinomas exist, each defined by unique characteristics. The
principal categories are: (i) PTC: Representing the most prevalent form, PTC
constitutes approximately 80% of all TC cases. It typically exhibits slow
growth but has a propensity to metastasize to neck lymph nodes. Nonetheless,
PTC generally responds well to treatment. (ii) FTC: Ranking as the second most
frequent type, FTC has the capability to invade blood vessels and spread to
distant body parts, although it is less prone to lymph node metastasis. (iii)
MTC: Arising from the parafollicular or C cells of the thyroid, which secrete
calcitonin, an increase in blood calcitonin levels may signal MTC. (iv) ATC:
ATC is a highly aggressive and rare TC variant, characterized by its rapid
spread to other neck regions and the body, making it challenging to treat. The
stratification of thyroid carcinomas is vital for selecting the optimal
treatment plan for individual patients, considering tumor dimensions,
location, patient age, and general health. The evolution of AI and ML has
significantly contributed to the automation and enhancement of thyroid
carcinoma classification accuracy. Various models have been devised to
categorize tumors based on medical imaging or genetic information.

For example, Liu and colleagues [66] highlight the fundamental importance of
support vector machine (SVM) in the detection of cancer. In a similar vein,
Zhang and their team [67, 68] introduce approaches utilizing deep neural
network (DNN) to distinguish between malignant and benign thyroid nodules in
US imagery. Moreover, the bi-directional LSTM (Bi-LSTM) model [69], shows
noteworthy precision in the classification of thyroid nodules. These
classification approaches create structured hierarchies crucial for organizing
knowledge and processes in the field of TC.

(b) Segmentation of thyroid carcinoma: Entails pinpointing and outlining the
area in an image that represents a thyroid tumor. The objective of this
segmentation is to segregate the targeted regions, like the thyroid tumor,
from adjacent tissues within medical images. This task can be achieved by
manual annotation by an experienced radiologist or via automated techniques
that employ ML algorithms [70, 71].

Segmentation plays a pivotal role in the detection of TC by enabling the
accurate isolation and analysis of the thyroid gland, along with any
potentially suspicious nodules or lesions present. Often detected through
medical imaging modalities like US, computed tomography (CT) scans, or
magnetic resonance imaging (MRI), TC requires the precise delineation of the
region of interest (ROI) for sound diagnostic judgements. Segmentation not
only facilitates the distinction of the thyroid gland from the surrounding
tissues but also supports the precise quantification of nodule dimensions and
volume, which are critical for evaluating the potential for malignancy.
Furthermore, it enables the extraction of significant image attributes such as
texture and shape, providing critical data for ML models or other analytical
methods to improve diagnostic precision. Segmented images also enhance visual
clarity, aiding radiologists and medical practitioners in the visual
assessment and interpretation of areas of concern within the thyroid, vital
for detecting abnormalities indicative of cancer. For longitudinal analyses,
segmentation is invaluable in tracking changes in the thyroid and nodules over
time, monitoring disease evolution or the efficacy of treatments. It also
plays a role in accurately locating biopsy sites for suspicious nodules,
guaranteeing targeted sample collection for cancer verification. In the
context of treatment planning, segmentation is instrumental in assessing the
tumor’s size and its relationship to vital anatomical structures, thereby
guiding therapeutic decisions. Moreover, the introduction of automated
segmentation technologies streamlines clinical workflows by minimizing manual
input and variability, empowering medical experts to dedicate more attention
to complex diagnostic activities. Consequently, the segmentation process in
TCD enhances precision, consistency, and confidence in diagnostics, markedly
impacting patient management and outcomes.

AI methods, especially convolutional neural network (CNN) and the U-Net
architecture, are becoming progressively popular for the segmentation of
thyroid carcinoma. Their growing preference is largely due to their capacity
to learn from and generalize across large datasets, significantly improving
the accuracy and dependability of the segmentation procedure.

(c) Prediction of thyroid carcinoma:

Prediction in TC involves utilizing diagnostic tools and ML models to estimate
the risk of development based on factors like genetic predisposition, gender,
age, radiation exposure, and lifestyle choices. It’s important to note that
predictions indicate a heightened risk rather than a definite outcome. Medical
practices often combine various predictive assessments to improve accuracy.
For example, ML algorithms developed from medical records can help distinguish
between benign and malignant nodules, facilitating early intervention.
Studies, such as one utilizing ANN and logistic regression (LR) [72], and
another employing a CNN to analyze over 10,000 microscopic images of TC [73],
demonstrate the application of predictive techniques in identifying TC risk,
showcasing advancements in AI-driven predictive modeling for more effective
treatment strategies.

###  2.3 SL-based TC classification

SL is a ML approach where the ai algorithm outcomes is based on data that is
explicitly labeled, in this discussion, related to medical information
concerning TC. The primary aim of supervised learning (SL) is to distinguish
among different types of TC using data that is both labeled and illustrative.

This data could include US images, genetic indicators, radiomic features,
patient demographics, or other pertinent details related to the diagnosis or
prognosis of TC. The provided labels clarify whether each case is related to
TC, and they may further specify details such as the type of thyroid carcinoma
or the stage of cancer. In a classification task, an SL algorithm might be
trained to differentiate between malignant and benign Thyroid nodules (TN)
using distinct features derived from medical imaging data. The training data’s
labels indicate the nature of each nodule, thereby training the algorithm to
classify new, unlabeled nodules accurately.

Similarly, the deployment of regression techniques in SL models, could be
developed to forecast the progression or outcome of TC using diverse, patient-
specific attributes. Here, the labels are associated with a continuous outcome
variable, such as survival duration or a metric indicating disease
advancement. It’s vital to highlight that the efficacy of these methodologies
is deeply dependent on the data’s quality and comprehensiveness. The more
accurate and complete the dataset, the more adeptly the algorithm can classify
or predict new cases. Moreover, to validate the utility of supervised learning
models in healthcare contexts, including the detection of thyroid carcinoma,
it’s essential to test these models on separate datasets and within real-world
clinical environments to confirm their accuracy and reliability [74, 75]. In
the following, a quick overview of frequently utilized ML algorithms is
described. (a) KNN and SVM:  The k-nearest neighbors (KNN) algorithm stands as
a non-parametric supervised learning method in ML, applied to both regression
and classification tasks. It operates by utilizing k-nearest training samples
for making predictions. In research conducted by Chandel et al., referenced in
[76], the KNN technique was used for classifying thyroid diseases using
variables like TSH, T4, and the presence of goiter. Furthermore, Liu and
colleagues [77] applied a fuzzy KNN model for distinguishing among
hyperthyroidism, hypothyroidism, and euthyroid (normal) cases. The need for
analyzing larger datasets in future studies has been emphasized, as noted in
[78].

SVM is a ML technique used for classification and regression tasks. In
research presented by Ma et al. [79], an SVM-based method was developed to
differentiate between benign and malignant thyroid nodules using a dataset
that included 98 TN samples, with 82 benign and 16 malignant cases. Another
study by Chang et al. [80] utilized six distinct SVMs for the classification
of nodular thyroid lesions, focusing on identifying the most significant
textural features for this purpose. The results indicated that the applied
methodology was effective in achieving accurate classification. Furthermore,
Dogantekin et al. [81] introduced a system that integrates Generalized
Discriminant Analysis with a wavelet SVM (GDA-WSVM) for the diagnosis of
thyroid nodules. This approach included steps for feature extraction,
classification, and testing, showcasing its application in effectively
diagnosing TC.

(b) Decision trees and logistic regression: decision trees (DT) learning is a
technique in data mining that uses a model for predictive decision-making. In
such a model, the outcomes are indicated by the leaves, and the branches
represent the input features. This method has been utilized in detecting
latent thyroid disorders, as evidenced by a range of studies, such as those
referenced in [82], [83], [84], and [85].

In the research presented in [86], LR was employed to pinpoint specific
characteristics of thyroid microcarcinoma among a group of 63 patients. This
analysis utilized data from both contrast-enhanced ultrasound (CEUS) and
traditional US evaluations. Furthermore, a significant study from northern
Iran, detailed in [87], used LR to investigate a large dataset encompassing
33,530 cases of TC. LR is a widely used binomial regression model within the
domain of ML.

###  2.4 USL-based TC classification

In the realms of AI and computer science, USL is the process of analyzing data
that hasn’t been previously labeled or annotated. Its primary goal is to
uncover the underlying structures within datasets that do not have predefined
labels. Contrary to supervised learning, which depends on labeled data for
evaluating its effectiveness, USL operates without such direct guidance,
presenting additional challenges in result assessment. Although USL algorithms
are capable of addressing more complex problems than their supervised
counterparts, they might also lead to increased uncertainty, sometimes
creating unintended categories or incorporating noise rather than identifying
clear patterns. Nonetheless, USL is considered an indispensable asset in AI,
offering the potential to detect patterns within data that may not be obvious
at first [88, 89].

####  2.4.1 Clustering

The objective of this strategy is to organize a dataset composed of TC
information into distinct, uniform groups that share similar features. This
process aids in the categorization of unlabeled data into malignant or benign
sections. Due to its straightforwardness, this technique has received
significant attention in numerous medical research areas, enhancing its
applicability to tasks like detecting DNA copy number variations [90],
identifying breast cancer [91], pinpointing cancer-related genes [92],
diagnosing skin cancer [93], and discovering brain tumors [94]. Clustering
methods also prove helpful in classifying cancer instances that are not
clearly defined [95].

A research documented in [96] employed clustering to determine factors
impacting the normal functioning of the thyroid gland. The use of PCA played a
key role in organizing the clusters and simplifying the data structure.
Additionally, an innovative automated clustering system for diagnosing TC was
developed, as described in [97], which recommended appropriate medication
treatments for hyperthyroidism, hypothyroidism, and normal cases. The study in
[98] explored the use of fuzzy clustering on thyroid and liver datasets from
the UCI repository, where fuzzy c-means (FCM) and possibilistic fuzzy c-means
(PFCM) algorithms were employed and their performances compared.

(a) KM: The K-means (KM) method is used for dividing data into partitions and
tackles a combinatorial optimization challenge. It is commonly used in USL,
categorizing observations into k distinct clusters. In the research presented
by Mahurkar et al. [99], the study investigates the application of ANN and an
improved K-Means algorithm to standardize raw data. This study employed a
thyroid dataset from the UCI repository, comprising 215 total instances.

(b) Entropy-based (EB): In the study conducted by Yang et al. [100], a novel,
parameter-free computational model called DeMine was introduced for the
prediction of microRNA regulatory modules. DeMine utilizes an information
entropy-based methodology, comprising three primary steps. The process begins
by converting the miRNA regulation network into a cooperative MRMs network. It
then proceeds to pinpoint miRNA clusters, aiming to maximize entropy density
within the specified cluster. The final step involves grouping co-regulated
miRNAs into their appropriate clusters, thereby finalizing the MRMs. This
technique enhances predictive precision and facilitates the identification of
a broader array of miRNAs, potentially acting as tumor markers in cancer
diagnosis.

###  2.5 DL-based TC classification

DL is a subset of ML and AI that focuses on the use of ANN for learning
representations. ANNs are information processing units composed of
interconnected non-linear units called neurons, effective in solving intricate
problems through information storage and management. A neural network
featuring several hidden layers where are often referred to as a DNN. The
network’s depth facilitates the extraction of increasingly abstract and
advanced features as data moves through its layers. By leveraging large neural
networks with several layers, DL can independently learn, create, and improve
data representations, which is why it’s known as ”deep” learning. Within the
realm of TC, DL is instrumental in several areas, including: (i)
Classification of image: DL techniques, for example CNN, are trained to
categorize thyroid US images, distinguishing between malignant and benign
nodules by analyzing texture and shape, and other features [101, 102, 103],
streamlining the process and assisting with the early identification of TC.
(ii) Analysis of disease: DL is used to examine cytopathological or
histopathological slide images, aiding in identifying and classifying cells of
cancerous. (iii) Analysis of genomic information: In this era DL models are
capable of analyzing genetic variations linked to the risk of TC. (iv)
Radiomics: DL models are adept at extracting Multidimensional information
obtained from radiographic images, contributing to more accurate and
individualized treatment strategies. (v) Predictive Analysis: By analyzing
electronic health records and patient information, DL models can forecast the
probability of an individual developing TC, facilitating early intervention.
Figure 4 illustrates the diverse applications of DL in the field of TC
diagnosis and management.

Figure 4: Comprehensive DL framework for detecting and classifying TC.

####  2.5.1 ELM and MLP

The extreme learning machine (ELM) model is distinguished by a single layer of
hidden nodes that possess randomly assigned weight distributions. Crucially,
the process of determining weights between the inputs and the hidden nodes to
the outputs is executed in a solitary step, rendering the learning mechanism
markedly more efficient than that of alternative models. The efficacy of the
ELM approach in diagnosing TD has been corroborated through various research
efforts, including those reported in [104], [105], [106], and [107].

The multilayer perceptron (MLP) is a type of feed-forward network that directs
data processing sequentially from the initial point to the final layer of
output. Within this architecture, each layer is made up of a different number
of neurons. Rao and colleagues [108] devised an innovative approach for
categorizing thyroid nodules employing an MLP integrated with a
backpropagation learning mechanism. Their design comprised four neurons in the
initial layer, three neurons in each of its ten concealed layers, and one
neuron in the terminal layer. In a separate effort to enhance the precision of
TD diagnosis, Hosseinzadeh et al. [109] utilized MLP networks. Their analysis
compared the efficacy of MLP networks against the backdrop of existing
research on TC classification, highlighting the superior performance of MLP
networks. Isa and associates [110] investigated the influence of MLP networks
with various activation functions to determine the most effective function for
the precise classification of challenging diseases, including thyroid disease
and cancer of breast. The study assessed a range of activation functions, such
as logarithmic, sinusoidal, neural, hyperbolic tangent, sigmoid and
exponential functions. It concluded that the function of hyperbolic tangent,
especially when utilized in conjunction with the backpropagation algorithm for
training, proved to be the most efficient for classifying TD. This conclusion
was also supported by the findings of Mourad et al. [111].

####  2.5.2 RBF and DAE

In the study by Erol et al. [112], ML methods were used for the TN
classification. The research utilized both MLP and radial basis function (RBF)
as activation functions. Notably, the RBF function outperformed the MLP in the
accurate classification of thyroid nodules, underscoring the critical role of
activation functions in function approximation. This research emphasizes their
significance in the classification and prediction of time series data,
especially concerning the diagnosis of TC.

DAE play a pivotal role in the identification of TC by adeptly deriving
significant features from US or histopathological imagery. As a subset of ANN,
denoising autoencoder (DAE) are primarily focused on the accurate
reconstruction of inputted data. Their usage extends to tasks such as reducing
dimensions and enhancing feature learning capabilities. The integration of
DAEs into the workflow for classifying thyroid carcinoma typically unfolds in
several phases: (i) initial data preprocessing, (ii) creation of perturbed
input data, (iii) training the DAE, (iv) extraction of relevant features, and
(v) execution of the classification procedure.

In the research presented by Ferreira et al. [113], a variety of six
autoencoder models were utilized for the purpose of classifying PTC,
incorporating strategies such as the stabilization of weights and network
fine-tuning. The architecture of these autoencoders, especially the encoding
layers, played an integral role in the effective integration of the network.
In a related study by Teixeira et al. [114], both DAEs and their stacked
configurations were applied to distill crucial features and pinpoint genes
relevant to TC diagnosis.

####  2.5.3 CNN and RNN

CNNs, a branch of DL models, stand out for their remarkable capabilities in
tasks such as image analysis and processing, including the classification of
medical images. Their efficiency in managing data structured in grids, like
images where the spatial relationship between pixels is crucial, makes them
particularly suited for these tasks.

The focus on CNN-based techniques for TCD, especially in automating nodule
identification and classification in US imagery [115], has grown
significantly. The ConvNet model, known for its reliance on convolution
operations vital for image recognition tasks [116], is a prime example of this
effort. Various architectures of CNN such as VGG [103], AlexNet [117], LeNet
[118], Squeeze Net [119], GoogLeNet [120], DenseNet [121], ResNet [122] and
are celebrated for their inclusion of convolutional, pooling, and fully
connected layers.

In a notable study by Li et al. [123], the efficacy of CNN models in
predicting TC was investigated, utilizing a dataset of 131,731 US images from
17,627 patients. Xie et al. [124] implemented models such as Inception-Resnet,
Inception, and VGG16 to differentiate malignant from benign tissues in 451
images of thyroid from the DDTI dataset, employing image augmentation to
mitigate data limitations prior to classification. Moreover, Koh et al. [125]
assessed the diagnostic accuracy of deep convolutional neural network (DCNN)
models against that of expert radiologists for identifying thyroid nodules in
US images, using a dataset of 15,375 images and showcasing the CNNE1 and CNNE2
models derived from DCNN for differentiating between malignant and benign
nodules. Liang et al. [126] introduced a DL based on CNN for classifying and
detecting thyroid and nodules of breast, comparing its performance with
traditional US imaging results. Figure 5 depicts the recent advancements in
classifying TC via CNN-based methods.

Figure 5: Synopsis of CNN-driven research in TC diagnosis with percentages for
accuracy, sensitivity, and specificity.

RNN belong to a category of ANNs characterized by connections between units
forming a directed graph across temporal sequences. This structure allows them
to leverage internal memory, making them adept at handling inputs of varying
lengths. Consequently, recurrent neural networks excel in tasks that require
understanding temporal dependencies, such as speech recognition, language
translation, and time-series analysis.

Within the realm of thyroid carcinoma classification, RNNs offer promising
capabilities for analyzing data that is sequential or time-sensitive. This
includes observing the evolution of clinical symptoms over time, tracking
changes in tumors using successive medical images or studying fluctuations in
gene expression associated with the onset of TC. For example, Chen et al.
(2017) utilized a hierarchical RNN structure to categorize thyroid nodules by
analyzing historical US reports [127]. Their model comprises three layers of
long-short-term-memory (LSTM) networks trained independently. The findings
from their study suggest that this hierarchical RNN approach surpasses
conventional models such as SVM + Unigrams, SVM + Bigrams, CNN, and LSTM in
accuracy, computational efficiency, and robustness. These benefits are
attributed to the RNNs’s memory mechanisms, which permit the retention of
information from previous states through feedback loops, thereby making RNNs
highly effective for cancer detection applications.

####  2.5.4 RBM and GAN

RBM feature a bipartite architecture with two layers: a visible layer for
input data and a hidden layer for processing. The defining characteristic of
restricted Boltzmann machines is the absence of intra-layer connections,
limiting connections to between-layer nodes only. This restriction simplifies
the training process, as each node in one layer exclusively connects to all
nodes in the opposite layer. Vairale et al. utilized RBMs to craft a
customized fitness recommendation system for individuals with thyroid issues,
demonstrating their practical application [128]. As generative models within
the ANNs spectrum, RBMs are notable for their bidirectional nature and
unsupervised learning capabilities, involving a visible layer of binary
variables and a hidden layer of interrelated binary variables, with the
learning mechanism heavily reliant on statistical methods.

ML framework is composed of two key elements: a generator and a discriminator.
The generator’s function is to convert a random input vector into a data point
that fits within the space of the dataset. Conversely, the discriminator
serves as a binary classifier, tasked with assessing whether input data,
originating either from the training dataset or produced by the generator, is
genuine. generative adversarial network (GAN) have found extensive
applications in medical diagnosis, notably in the detection of TN [129, 130].

####  2.5.5 PM and EM

PMs, which include bayesian networks, are crucial in both computer science and
statistics for depicting uncertainties and the complex interrelations among
variables. In the domain of computer science, these models are integral for
decision-making processes in ML, where they are used to quantify uncertainties
and map out intricate dependencies among data points. Bayesian networks, a
subset of probabilistic models, employ directed acyclic graphs to illustrate
the relationships between variables, proving invaluable in scenarios that
require reasoning in uncertain conditions, making predictions, and dealing
with partial information. Within the realm of statistics, probabilistic models
are essential for analyzing data, employing probabilistic principles to glean
significant insights and estimate parameters, thus deepening our comprehension
of multifaceted systems. Bayesian networks have been successfully applied in
the medical field to differentiate between benign and malignant TN [131, 132],
as well as in diagnosing diseases such as thyroid, hepatitis, and breast
cancer [133].

In the realm of oncology research, tackling the intricacies of cancer datasets
and enhancing the accuracy of detection frequently involves the use of
ensemble methods. This strategy splits the dataset into several subsets, upon
which a variety of ML algorithms are applied in parallel. The insights gained
from these individual algorithms are subsequently merged to derive a
comprehensive diagnosis. The main goal behind adopting ensemble methods is to
forge a superior predictive model tailored for the detection of TC. Such an
approach has been validated in multiple studies, including a significant one
conducted by Chandran et al. [134], where the authors underscored the
contribution of ensemble methods to a more profound data comprehension and
heightened diagnostic accuracy.

####  2.5.6 Bagging and Boosting

Within the domain of TCD, Bagging stands out as a key ensemble learning
strategy aimed at augmenting the precision and reliability of ML models. By
mitigating variance and preventing overfitting, this technique is extensively
used, particularly with decision trees, across various approaches. The primary
objective of Bagging is to bolster the efficacy of weaker classifiers, making
it particularly valuable in the context of screening for TC.

Bagging is a notable ensemble learning approach in TCD, aimed at boosting the
accuracy and consistency of ML algorithms. This technique achieves its
objectives by lowering variance and offering protection against overfitting.
It finds broad application in a variety of methods, with a particular emphasis
on decision trees. The main goal of Bagging is to improve the effectiveness of
weaker classifiers in the context of TC screening. In their research, Chen et
al. [135] presented feature bagging (FB) as an ensemble learning strategy
designed to reduce the correlation between models in an ensemble. FB
accomplishes this by training each model on randomly selected feature subsets
from the dataset, rather than using the full set of features. The utility of
FB is demonstrated in its ability to distinguish between benign and malignant
cases of TC [136]. Within the scope of USL, meta-algorithms play a crucial
role in reducing variance and improving the performance of weak classifiers,
effectively converting them into robust classifiers [137].

In the context of boosting, Pan et al. in their study [138] employed a novel
method called AdaBoost to identify TN, utilizing the widely recognized UCI
dataset. The classification was performed using the random forest method, with
PCA employed to retain data variance. Chen et al. [139], the Gradient tree
boosting (XGBoost) algorithm was highlighted as a powerful implementation of
gradient-boosted decision trees, with its application extending across
multiple research areas. This includes usage in civil engineering [140], time-
series analysis [141], sports and health monitoring [142], and the prediction
of ischemic stroke readmission [143]. Specifically, in the context of TC, the
XGBoost algorithm was employed by researchers to distinguish between benign
and malignant TN [144], offering a solution to the problem of obtaining
accurate diagnoses without the need for large datasets that DL models usually
require.

Table 2 serves as an overview of various research initiatives aimed at
identifying both benign and malignant forms of TC It outlines the classifiers
used, diseases focused on, datasets applied, research goals, and metrics for
assessment. This table helps categorize the AI techniques applied in TCD,
underlining their key roles in the domain.

Table 2: Summary of studies on identifying benign and malignant TC, sorted by
reference.

Ref. | AI Tech. | Classifier |  |  Objective |  DD |  Dataset |  APP |  SV
---|---|---|---|---|---|---|---|---
|  ML |  DL |  CNN |  SVM |  ELM |  Other |  |  |  |  |
[73] |  |  ✓ |  ✓ |  |  |  |  C |  TC |  PD |  Omics |  10068 images
[65] |  |  ✓ |  |  |  |  ✓ |  C |  TC |  PD |  NA |  NA
[76] |  ✓ |  |  |  |  |  ✓ |  C |  TD |  PD |  US |  7200 instances
[79] |  ✓ |  |  |  ✓ |  |  |  C |  TC |  PD |  US |  92 subjects
[82] |  ✓ |  |  |  |  |  ✓ |  C |  TC |  UCI |  US |  3739 patients
[84] |  ✓ |  |  |  |  |  ✓ |  C |  TC |  NA |  US |  NA
[85] |  ✓ |  |  |  |  |  ✓ |  C |  TC |  UCI |  US |  499 patients
[86] |  ✓ |  |  |  |  |  ✓ |  C |  TC |  PD |  US |  63 patients
[87] |  ✓ |  |  |  |  |  ✓ |  C |  TN |  PD |  US |  33,530 patients
[99] |  ✓ |  |  |  |  |  ✓ |  C |  TC |  UCI |  US |  215 instances
[100] |  ✓ |  |  |  |  |  ✓ |  C |  TC |  PD |  US |  734 cases
[104] |  ✓ |  |  |  |  ✓ |  |  C |  TD |  UCI |  US |  215 patients
[105] |  ✓ |  |  |  |  ✓ |  |  C |  TD |  UCI |  US |  215 patients
[106] |  ✓ |  |  |  |  ✓ |  |  C |  TD |  PD |  US |  187 patients
[108] |  ✓ |  |  |  |  |  ✓ |  C |  TD |  PD |  US |  7200 samples
[109] |  ✓ |  |  |  |  |  ✓ |  C |  TD |  UCI |  US |  7200 patients
[112] |  ✓ |  |  |  |  |  ✓ |  C |  TD |  PD |  US |  487 patients
[113] |  |  ✓ |  |  |  |  ✓ |  C |  PTC |  TCGA |  US |  18985 features
[114] |  |  ✓ |  |  |  |  ✓ |  C |  PTC |  TCGA |  Omics |  510 samples
[123] |  |  ✓ |  ✓ |  |  |  |  C |  TC |  PD |  US |  17627 patients
[124] |  |  ✓ |  ✓ |  |  |  |  C |  TC |  PD |  US |  1110 images
[126] |  |  ✓ |  ✓ |  |  |  |  C, P |  TN |  PD |  US |  537 images
[127] |  |  ✓ |  |  |  |  ✓ |  C |  TN |  PD |  US |  13592 patients
[128] |  |  ✓ |  |  |  |  ✓ |  C |  TD |  PD |  Fitness |  94 users
[131] |  ✓ |  |  |  |  |  ✓ |  C |  TD |  UCI |  US |  93 adult patients
[132] |  |  ✓ |  |  |  |  ✓ |  C |  TC |  NA |  US |  37 patients
[135] |  |  ✓ |  |  |  |  ✓ |  C |  TN |  PD |  US |  1480 patients
[145] |  |  ✓ |  ✓ |  |  |  |  C |  TC |  PD |  Omics |  482 images
[146] |  |  ✓ |  ✓ |  |  |  |  NA |  PTC, FTC |  NA |  FNAB |  NA
[147] |  |  ✓ |  ✓ |  |  |  |  C |  PTC |  PD |  FNAB |  370 MPG
[148] |  |  ✓ |  ✓ |  |  |  |  P |  PTC |  PD |  FNAB |  469 patients
[149] |  |  ✓ |  ✓ |  |  |  |  C |  TC |  DDTI |  US |  298 patients
[150] |  |  ✓ |  ✓ |  |  |  |  C |  TC |  PD |  US |  1037 images
[151] |  |  ✓ |  ✓ |  |  |  |  P |  TN |  PD |  US |  80 patients
[152] |  |  ✓ |  ✓ |  |  |  |  P |  TN |  PD |  US |  300 images
[153] |  |  ✓ |  ✓ |  |  |  |  C |  TC |  PD |  US |  459 labeled
[154] |  |  ✓ |  ✓ |  |  |  |  C |  TD |  ImageNet |  US |  2888 samples
[155] |  |  ✓ |  |  |  |  ✓ |  NA |  TC |  NA |  US |  NA
[156] |  |  ✓ |  |  |  |  ✓ |  C |  TC |  PD |  US |  1358 images
[157] |  ✓ |  |  |  |  |  ✓ |  C |  TC |  PD |  Surgery |  50 patients
[158] |  ✓ |  |  |  |  |  ✓ |  C |  TC |  PD |  US |  89 patients
[159] |  ✓ |  |  |  |  |  ✓ |  C |  TD |  PD |  Cyt |  447 patients
[160] |  ✓ |  ✓ |  |  |  |  ✓ |  C |  FTC |  PD |  FNAB |  57 smears
[161] |  ✓ |  ✓ |  |  |  |  ✓ |  NA |  FTC |  NA |  FNAB |  NA
[162] |  ✓ |  ✓ |  |  |  |  ✓ |  P |  TC |  TCGA |  Hist |  482 samples
[163] |  ✓ |  ✓ |  |  |  |  ✓ |  C |  TC |  PD |  FNAB |  1264 patients
[164] |  ✓ |  ✓ |  |  |  |  ✓ |  C |  TN |  PD |  US |  276 patients
[165] |  ✓ |  |  |  |  |  ✓ |  C, P |  FTC |  PD |  Hist |  94 patients
[166] |  ✓ |  |  |  ✓ |  |  |  C |  FTC |  PD |  Hist |  43 nuclei
[167] |  ✓ |  |  |  ✓ |  |  |  C |  TN |  PD |  US |  467 TN
[168] |  ✓ |  |  |  ✓ |  |  |  C |  PTC |  TCGA |  Omics |  500 patients
[169] |  |  ✓ |  |  |  |  ✓ |  PTC |  TCGA |  Omics |  115 slides |  NA
[170] |  ✓ |  |  |  |  |  ✓ |  C |  TN |  PD |  Omics |  121 patients

Abbreviation: Application (APP), Detected disease (DD), Thyroid cancer (TC),
Subjects for validation (SV), Private data (PD), Classification (C),
Prediction (P), Segmentation (S), Cytopathological (Cyt), Histopathological
(Hist), Microphotographs (MPG).

##  3 Standardized assessment criteria and commonly used datasets

###  3.1 Metrics

In this segment, we explore the standard metrics commonly utilized for
evaluating TD detection performance. These metrics act as critical benchmarks
for measuring the success of methodologies, underscoring the significance of
choosing the right metrics to assess ML models. A variety of DL metrics are
used to determine the efficiency of the suggested method in identifying TD. It
is important to note that certain metrics have been previously addressed in
[171]. The other metrics, particularly designed for image processing
applications in TD, are concisely outlined in Table LABEL:table:3. Figure 6
outlines the AI-based techniques for TCD.

Table 3: Summary of the metrics for classification and regression employed in assessing AI-driven methods for TCD. |  Metric |  Mathematical formula |  Description
---|---|---|---
4* Classification and Regression |  Specificity |  $\frac{T_{N}}{T_{N}+F_{P}}100\%$ |  This metric represents the proportion of accurately predicted negative samples out of all the negative samples.
|  RMSE |  $\left(\sqrt{1-\left(ER\right)^{2}}\right)\times SD$ |  This is the standard deviation of the predicted errors between the training and testing datasets, and a lower value indicates the classifier’s excellence.
|  Jaccard similarity index (JSI) |  $\frac{\left|A\cap B\right|}{\left|A\cup B\right|}=\frac{T_{P}}{T_{P}+F_{P}+F_{N}}$ |  Paul Jaccard introduced this method to measure both the similarity and diversity among samples.
|  Volumetric overlap error (VOE) |  $\frac{F_{P}+F_{N}}{T_{P}+F_{P}+F_{N}}$ |  Assess the likeness between the segmented area and the ground truth area. VOE quantifies the level of overlap between these two regions and is calculated as the ratio of the combined volume of the segmented and ground truth regions to the volume of their intersection.
|  Mean absolute error (MAE) |  $\frac{1}{N}\sum\limits_{i=1}^{N}\left|a_{i}-p_{i}\right|$ |  This measure indicates the average of the disparities between the real values and the predicted values.
|  Standard deviation (SD) |  $\sqrt{\sum\left(x-\mu\right)^{2}/N}$ |  It quantifies the degree of variability or spread within a dataset.
4* Statistical |  Correlation (Corr) |  $(\sum((x-\mu x)\cdot(y-\mu y)))/(\sqrt{(\sum(x-\mu x)^{2})}\cdot\sqrt{(\sum(y-\mu y)^{2}))}$ |  It characterizes the extent of correlation or connection between two or more variables.
|  mean reciprocal rank (MRR) |  $MRR=\frac{1}{\left|Q\right|}\sum_{i=1}^{\left|Q\right|}\frac{1}{rank_{i}}$ |  The MRR is a statistical measure used to assess the average reciprocal rank of outcomes for a set of queries, as explained in [172]. Here, ”ranki” denotes the position at which the first relevant document appears for the i-th query.
|  Kappa de Cohen |  $k=\frac{\Pr\left(a\right)-\Pr\left(e\right)}{1-\Pr\left(e\right)}$ |  This metric gauges the level of agreement between two assessors, considering chance as a baseline.
|  PSNR |  $10\cdot\log_{10}((MAX_{I}^{2})/MSE)$ |  It quantifies the proportion between the highest achievable signal power and the power of the noise that impacts the faithfulness of its portrayal.
9* Computer vision |  Visual information fidelity (VIF) |  $\frac{\sum_{j}I(C^{j};F^{j}/s^{j})}{\sum_{j}I(C^{j};E^{j}/s^{j})}$ |  It assesses the excellence of a reconstructed or compressed image or video in relation to the original signal. This evaluation considers how much visual information is retained in the processed image or video, accounting for the image’s spatial and frequency attributes.
|  Normalized cross-correlation (NCC) |  $\frac{\sum_{i=1}^{M}\sum_{i=1}^{N}(I(i,j)-R(i,j))^{2}}{\sum_{i=1}^{M}\sum_{i=1}^{N}I(i,j)^{2}}$ |  Assess the likeness between two images (or videos) by subtracting the mean value from each signal and subsequently normalizing the signals by dividing them by their standard deviation. Finally, compute the cross-correlation between the two normalized signals.
|  Structural content (SC) |  $\frac{\sum_{i=1}^{M}\sum_{j=1}^{N}I(i,j)^{2}}{\sum_{i=1}^{M}\sum_{j=1}^{N}R(i,j)^{2}}$ |  An elevated structural content value indicates that the image possesses lower quality.
|  Noise visibility function (NVF) |  $\mathrm{Normalization}\left\\{\frac{1}{1+\delta_{bloc}^{2}}\right\\}$ |  This calculates the texture information within the image, where $\delta_{bloc}$ represents the variance in luminance.
|  Visual signal to noise ratio (VSNR) |  $10\log_{10}\left(\frac{C^{2}(I)}{(VD)^{2}}\right)$ |  This approach sets distortion thresholds using contrast computations and wavelet transforms. VSNR is deemed excellent if distortions are below the threshold. It uses RMS contrast ($C(I)$) and visual distortion ($VD$) as defined in [173].
|  Weighted signal-to-noise ratio (WSNR) |  $10\log_{10}\left(\frac{\sum_{u=0}^{M-1}\sum_{v=0}^{N-1}\left|A(u,v)C(u,v)\right|^{2}}{\sum_{u=0}^{M-1}\sum_{v=0}^{N-1}\left|A(u,v)-B(u,v)C(u,v)\right|^{2}}\right)$ |  It relies on the contrast sensitivity function (CSF), with $A(u,v)$, $B(u,v)$, and $C(u,v)$ denoting the Discrete Fourier Transforms (2D TFD), as described in [174].
|  Normalized absolute error (NAE): |  $\frac{\sum_{i=1}^{M}\sum_{j=1}^{N}\left|I(i,j)-R(i,j)\right|}{\sum_{i=1}^{M}\sum_{j=1}^{N}\left|I(i,j)\right|}$ |  This metric assesses the precision of an ML model’s predictions by quantifying the discrepancy between predicted and actual values relative to the range of actual values.
|  Laplacian mean square error (LMSE) |  $\frac{\sum_{i=1}^{M}\sum_{j=1}^{N}\left[L(I(i,j))-L(R(i,j))\right]^{2}}{\sum_{i=1}^{M}\sum_{j=1}^{N}\left[L(I(i,j))\right]^{2}}$ |  It is a modified version of Mean Square Error (MSE), utilizing the Laplacian distribution instead of the Gaussian distribution. $L(I(i,j))$ represents the Laplacian operator.

###  3.2 Datasets for TC

In the context of TC research, numerous datasets have been developed to
support the testing and validation of ML algorithms and models. This step is
crucial, given the significant challenge of compiling these datasets within
the endocrine ML field. Table 4 lists examples of publicly available TCDs that
are used for detecting TC.

Table 4: Instances of publicly available TCDs utilized in the identification of TC.  Ref. |  TCD |  Description |  Link
---|---|---|---
[175] |  THO |  This dataset is designed to investigate the fundamental causes and effects of TD through the application of diverse omics approaches, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics. |  Visit THO datasets
[176] |  TDDS |  The dataset used for classification encompasses 5 features and 7200 instances, featuring a mix of 15 categorical and 6 numerical attributes. The classes within this dataset comprise hypothyroid, hyperfunction, and subnormal functioning. |  Visit TDDS datasets
[177] |  KEEL |  The KEEL dataset offers a collection of benchmarks for assessing the performance of different learning approaches, including semi-supervised classification and USL. It encompasses 21 features, 7200 instances, and 3 classes. |  Visit KEEL datasets
[178] |  GEO |  The GEO database serves as a repository for genomics data. It is specifically structured to archive gene expression datasets, arrays, and sequences within gene expression omnibus (GEO). |  Visit GEO datasets
[179] |  DDTI |  The digital database thyroind image (DDTI) dataset acts as an essential tool for both researchers and novice radiologists aiming to create algorithm-driven computer-aided diagnosis (CAD) systems for analyzing TN. It contains 99 cases and 134 images. |  Visit DDTI datasets
[180] |  NCDR |  The national cancer data repository (NCDR) functions as a repository for healthcare and research purposes, aimed at documenting every reported instance of cancer within England. The data originates from the Office for National Statistics. |  Visit NCDR datasets
[181] |  PLCO |  The National Cancer Institute backs the prostate, lung, colorectal, and ovarian (PLCO) cancer screening trial, which focuses on identifying the primary factors influencing cancer incidence in both genders. This trial encompasses records from 155,000 participants and includes comprehensive studies on TC incidence and mortality. |  Visit PLCO datasets
Figure 6: Summary of techniques for the TCD based in AI [182, 183, 184, 185,
186, 187, 188, 189, 190].

##  4 Features selection and extraction

This method evaluates the importance of different attributes by analyzing the
relationships between features linked to cancer. In their research, Cui et al.
[191] introduced a feature selection technique that leverages the Relief
algorithm to enhance its performance.

###  4.1 Selection methods

This part focuses on highlighting the methods commonly employed in the
classification process. The primary goal is to identify and select relevant
features that can enhance the accuracy of classification, simultaneously
eliminating non-essential variables.

(a) IG: This method simplifies the classification of TC attributes by
assessing the probability of cancer presence through the comparison of entropy
levels pre and post-evaluation. Generally, a greater gain value signifies
reduced entropy. Although information gain (IG) provides numerous advantages,
it also comes with specific limitations and challenges:

  * •

Advantages: This approach enhances model performance by identifying relevant
features, reducing data clutter, and improving accuracy. By simplifying the
feature set, it makes complex models more manageable and speeds up both
training and inference, which is vital for real-time applications. It helps
prevent overfitting through the removal of non-essential attributes, leading
to models that are simpler and easier to understand, thereby aiding in
decision-making processes. Additionally, it lowers costs by focusing on
critical data, allows for easier application to new datasets, and provides
insights into the significance of features for a better understanding.

  * •

Disadvantages: Typically, this method assesses features on an individual
basis, which may lead to missed crucial interactions between them. It often
favors categorical features over continuous ones, especially in the absence of
proper discretization. This approach risks ignoring the broader context,
potentially leading to a substantial loss of information. Moreover, it can
show a preference for majority classes and may not be well-suited to certain
model types. Handling data with many dimensions poses difficulties, and the
arbitrary decision-making in setting thresholds can affect the results.

The IG technique has found extensive application in various aspects of cancer
diagnosis. Its applications range from isolating genes that provide meaningful
insights to enhance the precision of cancer classification, to determining
treatment variables for breast cancer through entropy metrics. It has also
been used for the systematic analysis and structuring of breast cancer-related
medical data, compressing gene dimensions in datasets involving multiple
cancer types, and removing genes that are either irrelevant or redundant in
cancer studies. Moreover, the integration of IG with SVM, known as the IG-SVM
method, has been adopted, where the IG outcomes serve as input for the LIBSVM
classifier, illustrating its versatility in feature selection and enhancement
of classification processes.

(b) CFS: The correlation-based feature selection (CFS) technique is commonly
applied to examine the relationships between various cancer-related
attributes. Although CFS offers a range of advantages, there are also notable
limitations associated with its use in feature selection within ML:

  * •

Advantages: CFS is valued for its straightforwardness, ability to identify
linear correlations, capability to decrease dimensionality, and potential to
boost model efficiency and clarity. This method supports quicker model
training and prediction, exhibits robustness against outliers, and
accommodates the incorporation of expert insights. Moreover, it enhances the
effectiveness of other methodologies, provides opportunities for visualization
and deeper understanding, lowers expenses, and enables the conduct of
sensitivity analyses.

  * •

Disadvantages: It’s important to recognize that this approach may miss
complex, nonlinear relationships between features and the outcome variable.
Additionally, it can be vulnerable to multicollinearity, where features are
highly correlated with each other, requiring additional preprocessing steps.
Careful consideration of the specific issue and dataset at hand is crucial
when applying this method.

The CFS algorithm has been widely incorporated into feature selection
strategies to enhance classification outcomes across various studies. For
example, Ashraf et al. employed it for analyzing datasets related to thyroid,
hepatitis, and breast cancer from the UCI ML repository, as documented in
[192]. A hybrid methodology that integrated learning algorithm tools with
feature selection techniques for diagnosing diseases, including CFS as a key
element, was introduced in [133]. In [193], researchers utilized CFS for
feature selection within microarray datasets, successfully minimizing data
dimensionality and pinpointing significant genes. A combined model that
blended CFS with binary particle swarm optimization (BPSO) was developed in
[194] for cancer classification, applied to 11 standard microarray datasets.
Additionally, the CSVM-RFE technique, which integrates CFS, was applied in
[195] to diminish the feature set in cancer research by removing non-essential
elements. Moreover, CFS methodologies were utilized in [168] for the
identification of key RNA expression features.

(c) Relief (RA): The Relevance Analysis (RA) is an effective technique used in
feature selection, evaluating the discriminative power of features between
classes through score assignment. RA has its own array of advantages and
disadvantages:

  * •

Advantages: Relief offers several benefits in feature selection, including its
robustness against noisy data, capability to handle both continuous and
categorical features, and ability to detect feature interactions without
assuming their independence. It also reduces bias in datasets with imbalanced
classes, eliminates the need for model training, and facilitates sensitivity
analysis. These attributes make Relief an advantageous tool for feature
selection in various data scenarios.

  * •

Disadvantages: Relief exhibits significant computational complexity, affecting
its applicability to large datasets. Its performance is sensitive to
parameters, particularly the choice of the number of nearest neighbors (k),
which can be challenging to optimize. The stability of Relief is also
affected, with variations in the dataset leading to different selections of
features. Furthermore, it is designed solely for use within supervised
learning contexts, struggles with non-metric features, and necessitates
adaptations for handling multiclass classification scenarios.

This method evaluates the importance of different features by exploring the
relationships between variables related to cancer. In their research, Cui et
al. [191] suggested a feature selection strategy that employs the Relief
algorithm to enhance its effectiveness.

###  4.2 Extraction methods

(a) PCA: PCA has been widely recognized in numerous researches for its
effectiveness in reducing data dimensionality and decoupling cancer-related
features. PCA is praised for its ability to decrease dimensions and reveal
patterns, although it may compromise on interpretability and is optimally used
with linear correlations. For example, Shankarlal et al. (2020) implemented
PCA to refine feature selection for TC via the DTCW transformation [64].
Soulaymani et al. (2018) investigated PCA’s capability in distinguishing
various TC subtypes, such as papillary, follicular, and undifferentiated types
[65]. Additionally, O et al. (2019) assessed PCA and linear discriminant
analysis in the classification of Raman spectra for different TC subtypes
[196]. Selaru et al. (2004) also utilized PCA on cDNA microarray data to
explore the genetic underpinnings of breast cancers [197].

(b) Texture description: Texture analysis is a highly regarded technique for
extracting related data in TC segmentation, classification, and prognosis
efforts. The scientific community has developed various texture analysis
methods, including wavelet transforms, binary descriptors, and statistical
descriptors, among others. Specifically, the discrete wavelet transform (DWT)
has garnered significant interest for its exceptional capability in data
decorrelation. Although texture analysis is beneficial for distinguishing
textures, it can be affected by changes in lighting conditions and does not
inherently understand semantic content, which may limit its application in
complex visual tasks. Wavelet-based methods have been extensively applied in
detecting TC. For example, Sudarshan et al. [198] applied wavelet techniques
to identify cancerous areas in thyroid, breast, ovarian, and prostate tumors.
Additionally, Haji et al. [199] used texture data for the diagnosis of thyroid
nodule malignancy employing a 2-level 2D wavelet transform. Further
contributions to this field are documented in studies such as [200] and [201].

(c) AC: The active contour (AC) model, a versatile framework often used in
image processing, was initially introduced by Kass and Witkin in 1987. AC is
known for its ability to adjust to complex shapes, yet it faces challenges
such as sensitivity to initial placements and issues with overlapping figures.
Various strategies have been developed to address these challenges in contour
segmentation using deformable curve models. These models have seen significant
application in TCD, as evidenced by research conducted by [202], [203], and
[204].

(d) LBP and GLCM: local binary patterns (LBP) are descriptors used in computer
vision for identifying textures or objects in digital images. They are
appreciated for their straightforwardness and ability to distinguish features
effectively. However, LBP may be vulnerable to noise and often requires tuning
of parameters for optimal performance. The LBP method was employed in TCD, as
illustrated in a study by Yu et al. [200]. Furthermore, the integration of LBP
with DL has been explored for distinguishing between benign and malignant TN,
as seen in the studies by Xie et al. [205] and Mei et al. [206].

The gray-level co-occurrence matrix (GLCM) serves as a tool to depict the
occurrence frequency of pixel value pairs at a predetermined distance within
an image. It is particularly useful for texture analysis and the
identification of distinctive features. Nevertheless, GLCM faces challenges
such as sensitivity to image variations, high computational demands, and the
need for careful parameter tuning. Song et al. [207] leveraged GLCM for
feature extraction to differentiate among various types of TC. Additionally,
Dinvcic et al. [208] employed GLCM in a comparative study to investigate the
differences between patients with Hashimoto’s thyroiditis-associated PTC and
those with Hashimoto’s thyroiditis only.

(e) ICA: In independent component analysis (ICA), data is decomposed into a
set of independent contributing features to aid in feature extraction. ICA is
adept at identifying statistically independent components, making it valuable
for tasks like source separation. Its strengths lie in uncovering non-linear
relationships and facilitating data compression. Nonetheless, ICA faces
challenges due to assumptions about the data mixing process and can be
difficult to interpret. ICA is applied for disentangling multivariate signals
into their separate constituents. In the research conducted by Kalaimani et
al. [209], ICA was used to isolate 29 attributes as independent and
significant features for categorizing data into hypothyroid or hyperthyroid
groups through a SVM.

A portrayal of the techniques derived from DL utilized in diagnosing TC is
provided in Table 5.

Table 5: Summary of features methods based on DL conducted in the diagnosis of TC. Ref. |  Year |  Classifier |  Features |  Contributions
---|---|---|---|---
[210] |  2017 |  KNN |  FC/IG |  Minimize data duplication and decrease processing duration. KNN addresses absent dataset values, while ANFIS receives the modified data as input.
[211] |  2017 |  SVM |  FC/CFS |  Retrieve the geometric and moment characteristics, while specific SVM classifier kernels categorize the acquired features.
[111] |  2020 |  CNN |  FC/R |  Utilize both ML techniques and feature selection algorithms, specifically Fisher’s discriminant ratio, Kruskal-Wallis analysis, and Relief-F, for the examination of the SEER database.
[212] |  2022 |  CNN |  FE/PCA |  This study mitigated the impact of imbalanced serum Raman data on prediction outcomes by employing an oversampling technique. Subsequently, the dimension of the data was reduced with PCA before applying RF and the Adaptive Boosting for classification.
[213] |  2012 |  Boosting |  FE/TD |  Integrate CAD with DWT and extract texture features. Utilize the AdaBoost classifier to classify images into either malignant or benign thyroid images based on the extracted features.
[214] |  2021 |  CNN |  FE/AC |  Improve image quality, perform segmentation, and extract multiple features, including both geometric and texture features. Each feature set is subsequently classified using MLP and SVM, leading to the classification of either malignant or benign cases.
[215] |  2020 |  SVM |  FE/LBP |  Deep features are obtained through CNN, and they are merged with manually crafted features, which include histogram of oriented gradient (HOG) and scale-invariant feature transforms, to generate combined features. These combined features are subsequently employed for classification via an SVM.
[216] |  2019 |  SVM |  FE/GLCM |  Apply a median filter to mitigate noise and outline the contours before feature extraction from thyroid regions, encompassing GLCM texture features. Subsequently, employ SVM, RF, and Bootstrap Aggregating (Bagging) to differentiate between benign and malignant nodules.
[209] |  2019 |  SVM |  FE/ICA |  A multi-kernel-based classifier is employed for thyroid disease classification.

##  5 TCD using vision transformers

###  5.1 Detection techniques

Transformer models are a type of DL model introduced in [217]. They have since
become the foundation for many state-of-the-art natural language processing
(NLP) and ML models. Transformers are designed to handle sequential data,
making them suitable for various tasks beyond NLP as well. Transformers in TCD
enable efficient analysis of medical data, including literature and patient
records, offering high accuracy. They uncover patterns, risk factors, and
diagnostic clues, aiding early detection. This technology enhances diagnosis
speed, fuels data-driven research, and promises improved patient care and
oncology advancements [218, 219]. Several studies have proposed the use of DL
models, particularly transformer-based models, for detecting TC from US
images. Here are some of these studies, for example, researchers in [220]
developed a diagnostic system using DL (Deit, Swin Transformer, and Mixer-MLP)
and metaheuristics to improve thyroid abnormality detection. They employed
feature extraction and dimensionality reduction techniques, then evaluated
multiple classifiers. The MEREC-TOPSIS MCDM method ranked the models, leading
to the selection of the best-performing models for ensemble learning. The
PCA+FOX optimization-based feature selection and random forest model achieved
high accuracy on US and histopathological datasets, surpassing existing
methods. This innovative approach eases the burden on healthcare professionals
by enhancing TC diagnosis. The study [221], addressed the challenge of
extracting important thyroid nodule characteristics from clinical narratives
in US reports using NLP. A team of experts developed annotation guidelines and
tested five transformer-based NLP models. Their GatorTron model, trained on a
substantial text corpus, outperformed others, achieving the best F1-scores for
extracting 16 thyroid nodule characteristics and linking them to nodules. This
pioneering work enables improved documentation quality of thyroid US reports
and enhances patient outcomes assessment through electronic health records
analysis. In [222], the study introduces a novel Boundary-preserving assembly
Transformer UNet (BPAT-UNet) for precise US thyroid nodule segmentation. This
network incorporates a boundary point supervision module (BPSM) for boundary
refinement and an adaptive multi-scale feature fusion module (AMFFM) for
handling various scales of features. Additionally, an assembled transformer
module (ATM) improves boundary constraints and small object detection. Results
demonstrated significantly improved segmentation accuracy compared to
classical networks, achieving Dice similarity coefficients of 85.63% and
81.64% and HD95 values of 14.53 and 14.06 on private and public datasets,
respectively. Chen et al. [223], introduces Trans-CEUS, a spatial-temporal
transformer-based model for real-time microvascular perfusion analysis using
CEUS as it shown in Figure 7. It combines dynamic swin-transformer and
collaborative learning to accurately segment lesions with unclear boundaries,
achieving a Dice similarity coefficient of 82.41%. The model also attains a
high diagnostic accuracy of 86.59% for distinguishing malignant and benign TN.
This pioneering research highlights the effectiveness of transformers in CEUS
analysis and offers promising outcomes for thyroid nodule segmentation and
diagnosis from dynamic CEUS datasets.

(a)

(b)

Figure 7:  (a) The general framework of the suggested HEAT-Net, (b) Details of
the swin-transformer block [223]

.

DL has been instrumental in medical image segmentation, particularly for
thyroid glands in US images. However, existing models face issues like the
loss of low-level boundary features and limitations in capturing contextual
features. In response, a hybrid transformer UNet (H-TUNet) is introduced in
[219]. It combines a 2D Transformer UNet with a multi-scale cross-attention
transformer and a 3D Transformer UNet with self-attention to improve
representation and contextual information. The end-to-end network was
evaluated on thyroid segmentation datasets, outperforming other methods in
benchmark tests. H-TUNet shows promise for thyroid gland segmentation in US
sequences. Set classification involves predicting a single label from sets
with multiple instances, like pathology slides or medical text data. State-of-
the-art methods often use complex attention architectures to model set
interactions. However, when labeled sets are limited, as in medical
applications, these architectures are challenging to train. To tackle this
issue, a kernel-based framework is introduced in [224], connecting affinity
kernels and attention architectures. This leads to simplified ”affinitention”
nets, which are applied to tasks like Set-Cifar10 classification, thyroid
malignancy prediction, and patient text triage. Affinitention nets deliver
competitive results, outperforming heuristic attention architectures and other
methods. jerbi et al. in 2023 [225], a DL approach, incorporating CNNs and
vision Transformers, was employed to classify thyroid US images as either
malignant or benign. A deep convolutional generative adversarial network
(DCGAN) was used to address data scarcity and imbalance. Various models,
including VGG16, EfficientNetB0, ResNet50, ViT-B16, and Hybrid ViT, were
trained with both softmax and SVM classifiers. The hybrid ViT model, with SVM
classification, outperformed others, achieving a 97.63% accuracy, showing
promise for aiding doctors in diagnosing thyroid patients more effectively.

US is a valuable clinical screening tool, but segmenting lesions or organs in
US images is challenging due to artifacts and low contrast. In this study
[226], the authors introduces a novel U-shape segmentation model (Figure 8)
combining CNN and transformer structures to integrate local and long-range
information. It uses coordinate residual blocks (CdRB) to encode position
data, channel-enhanced self-attention-based transformers (ECAT) for global
feature enhancement, and a dual attention module (CDAM) for feature
correlation and edge accuracy. The method outperforms state-of-the-art methods
across various datasets, demonstrating adaptability and robustness in US image
segmentation, potentially serving as a general segmentation tool.

Figure 8: An example of the basic structure of encoder HEAT-Net for TC
segmentation [226].

The study [227] addresses the challenge of accurately diagnosing malignant TN
through US imaging. Existing CAD methods often struggle to maintain precise
shape information and capture long-range dependencies. The proposed
Transformer fusing CNN Network (TCNet) utilizes a large kernel module (LKM) in
a CNN branch for shape feature extraction and an enhanced Transformer module
(ETM) in another branch for remote pixel connections. A Multiscale fusion
module (MFM) integrates feature maps from both branches. Comparisons with
other methods demonstrate TCNet’s superiority and effectiveness in nodule
segmentation. Hypoparathyroidism is a major concern post-TC surgery, affecting
patients’ quality of life. Identifying and locating parathyroid glands via US
images before surgery can help protect them. In [228] a dual-branch
contextual-aware network (DCA-Net) with Transformer is proposed to reduce
hypoparathyroidism incidence. It combines a Transformer for global context
extraction and a feature encoder branch for local feature aggregation. A
channel and spatial fusion module (CSFM) integrates information from both
branches. The DCA-Net effectively addresses detail loss, establishing global
and local feature dependencies. Experiments with an US image dataset
demonstrate superior performance compared to existing methods. The thyroid
gland plays a crucial role in regulating the human body’s functions, making
the identification of TN from US images important for medical diagnosis.
However, the automatic segmentation of these nodules is challenging due to
their heterogeneous appearance and background similarities. This study [70]
presents a novel framework AMSeg based on Swin-Unet architecture presented in
Figure 9, which employs multiscale anatomical features and late-stage fusion
through adversarial training to address these challenges. Experimental results
demonstrate the superiority of AMSeg in thyroid nodule segmentation, achieving
high dice, Hd95, Jaccard, and precision values. This end-to-end network offers
promise for clinical applications, potentially replacing manual segmentation
methods.

Figure 9:  The Swin-Unet architecture [70].

In this study [229], the authors harnessed natural language processing with a
bidirectional encoder representations from transformers (BERT) classifier to
analyze unstructured clinical text data pertaining to recurrent papillary TC
(PTC) diagnosis. The BERT model achieved exceptional performance, boasting a
98.8% accuracy in binary PTC recurrence classification. This approach
streamlines the handling of unstructured patient information, eliminating the
need for labor-intensive data refinement, and holds significant promise for
training AI models in healthcare. The variability in features between benign
and malignant TN, particularly in TI-RADS level 3, can lead to inconsistent
diagnoses and unnecessary biopsies. To address this in [230], TC-ViT, a Vision
Transformer-based model, utilizes contrast learning to enhance diagnostic
accuracy and biopsy specificity. By incorporating global and local features,
this model minimizes the distinction between nodules of the same category.
Test results indicate an accuracy of 86.9%, outperforming classical DL models.
TC-ViT offers automatic classification of TI-RADS 3 and malignant nodules in
US images, promising improved computer-aided diagnosis and precise analysis.
Optical coherence tomography (OCT) can aid in distinguishing normal and
diseased thyroid tissue during surgery, but interpreting OCT images is
challenging. In this study [231] explored various DL models for classifying
thyroid diseases using 2D and 3D OCT data from 22 surgical patients with
thyroid pathologies. The 3D vision transformer model achieved the best
performance, with an MCC of 0.79 (accuracy of 0.90) for normal versus abnormal
classification. Custom models also excelled on open-access datasets. These
findings suggest that combining OCT with DL can enable real-time, automatic
identification of diseased tissue during thyroid surgery.

Accurate segmentation of thyroid nodules in US images is crucial for early TC
diagnosis. Addressing the challenges posed by weak image edges and complex
thyroid tissue structure, the study [232] introduces a local and context-
attention adaptive network (LCA-Net). It combines local features from
convolutional neural networks and global context from transformers, improving
edge information capture. The model incorporates specific modules to handle
different nodule sizes and positions, enhancing generalization. LCA-Net
outperforms existing models on public datasets, demonstrating its potential
for precise thyroid nodule diagnosis in clinical settings. In this study
[233], the authors focuses on improving the prediction of lymph node
metastasis in papillary thyroid carcinoma by combining whole slide
histopathological images (WSIs) and clinical data. They introduce a
transformer-guided multi-modal multi-instance learning framework that
effectively groups high-dimensional WSIs into low-dimensional feature
embeddings and explores shared and specific features between modalities. The
approach achieved an impressive AUC (area under the curve) of 97.34% on their
dataset, outperforming state-of-the-art methods by 1.27%, highlighting its
potential in improving precision medicine decisions based on multi-modal
medical data fusion. Diagnosing lymph node metastasis in papillary thyroid
carcinoma typically relies on analyzing large whole slide histopathological
images (WSIs). To enhance accuracy, a novel transformer-guided framework is
introduced in [234], leveraging transformers in three critical aspects. It
incorporates a lightweight feature extractor, a clustering-based instance
selection scheme, and a Transformer-MIL module for effective feature
aggregation. The model further benefits from an attention-based mutual
knowledge distillation (AMKD) paradigm. Experimental results on a WSI dataset
outperform state-of-the-art methods by a significant margin, achieving a 2.72%
higher AUC. Xiao et al. in [235] aim to address the challenges of diagnosing
TC, particularly in cases where US images suffer from noise and artifacts,
leading to a certain misdiagnosis rate in clinical practice. They highlight
the need for further diagnosis using plain and contrast-enhanced CT scans.
While plain CT provides valuable information, contrast-enhanced CT offers
better contrast and can reflect organ margin erosion, a crucial symptom for TC
diagnosis. However, the latter relies on the use of a contrast agent and
exposes the patient to ionizing radiation. To mitigate these challenges, the
authors propose an improved Unet architecture. Their approach involves using a
convolutional Transformer module to learn global information from high-
dimensional features. They also incorporate a texture feature module to
extract local texture information from plain CT scans and integrate edge
information obtained from superpixels as prior knowledge. The ultimate goal is
to generate enhanced CT images with clear texture and higher quality,
providing a valuable tool for TC diagnosis without the need for contrast
agents and ionizing radiation. Histopathological images carry valuable
information for tumor classification and disease prediction, but their size
hinders direct use in CNNs. This study [236] introduces Pyramid Tokens-to-
Token VIsion Transformer (PyT2T-ViT), a lightweight architecture with multiple
instance learning based on the vision Transformer. PyT2T-ViT uses Token-to-
Token ViT (T2T-ViT) for feature extraction to reduce model parameters. It also
incorporates an image pyramid to capture local and global features,
significantly reducing computation. Experiments on thyroid pathology images
yield superior results compared to CNN-based methods, balancing accuracy and
efficiency. The authors in [237] aim to utilize multi-instance learning for
the diagnosis of TC based on cytological smears. These smears lack
multidimensional histological features, necessitating the mining of contextual
information and diverse features for improved classification performance. To
address these challenges, they introduce a novel algorithm called PyMLViT,
which consists of two core modules. First, the pyramid token extraction module
is designed to capture potential contextual information from smears. This
module extracts multi-scale local features using a pyramid token structure and
obtains global information through a vision transformer structure with a self-
attention mechanism. Second, they construct a multi-loss fusion module based
on the conventional multi-instance learning framework. To enhance the
diversity of supervised information, they carefully allocate bag and patch
weights and incorporate slide-level annotations as pseudo-labels for patches
during training.

In Table 6, transformer-based models’ performance (in %) for TC diagnosis is
summarized.

Table 6: Summary of transformer-based models’ performance (in %) for TC
diagnosis

Ref. |  Transformer |  Task |  ACC |  SEN |  SPE |  AUC |  F1-score |  Improvement
---|---|---|---|---|---|---|---|---
[220] |  Swing |  C |  99.13 |  – |  – |  99.13 |  98.82 |  The diagnosis accuracy and the extract features of thyroid abnormality detection has been improved. The redundant features has reduced to avoid the overfitting.
[221] |  BERT, RoBERTa, LongFormer, DeBERTa, and GatorTron |  C |  97.10 |  98.80 |  92.80 |  – |  96.50 |  The proposed model can achieve satisfactory classification accuracy and identified a large numbre of characteristics comparable to experienced radiologists and can save time and effort as well as deliver potential clinical application value
[222] |  BPAT-UNet |  S |  – |  – |  – |  – |  – |  The proposed BPAT-UNet displays superior segmentation performance in visualization results and evaluation metrics
[223] |  Trans-CEUS |  S |  86.59 |  – |  – |  – |  – |  Demonstrated significant improvement when compared to previous approaches, showcasing its effectiveness in the tasks of lesion segmentation and thyroid nodule diagnosis.
[219] |  H-TUNet |  S |  – |  – |  – |  – |  – |  The 2D and 3D Transformer UNet can distinguish the thyroid regions from other anatomies more effectively than the state-of-the-art segmentation models.
[224] |  Network |  C |  – |  – |  – |  91.3 |  – |  Attention nets outperform complex attention-based architectures and other competing methods in tasks such as thyroid malignancy prediction.
[225] |  ViTs |  C |  97.63 |  – |  – |  – |  96.67 |  The SVM classification produces better performance than the Softmax classification for all of the models with the Hybrid ViT
[226] |  CNN |  S |  – |  – |  – |  – |  – |  The proposed method showcases excellent adaptability and robustness in US image segmentation and can potentially be a general US segmentation tool.
[227] |  ETM |  S |  – |  – |  – |  – |  – |  Extract the precise shape features of malignant thyroid and establish the remote connection between thyroid nodule pixels
[228] |  Bottleneck |  S |  – |  – |  – |  – |  – |  They proposed that DCA-Net can effectively mitigate the loss of details
[70] |  Swing |  S |  – |  – |  – |  – |  – |  The method can handle blurred and uneven tissue regions during the thyroid nodule segmentation process and enhanced prediction of accurate nodule edges.
[229] |  BERT |  C |  – |  – |  – |  – |  88.00 |  Analyze unstructured clinical text information on diagnosis of the recurrent PTC efficiently.
[230] |  ViT |  C |  86.90 |  87.10 |  86.10 |  – |  92.4 |  Improve accuracy of diagnosis and specificity of biopsy recommendations. Minimize the representation distance between nodules of the same category
[231] |  3D vision |  C |  90.00 |  – |  – |  – |  – |  Efficiently classifying thyroid diseases
[232] |  LCA-Net |  S |  – |  – |  – |  – |  – |  The superiority of the proposed LCA-Net for thyroid nodule segmentation
[233] |  GMMMIL |  P |  93.88 |  – |  – |  97.34 |  94.65 |  Experimental results on the collected lymph node metastasis dataset demonstrate the efficiency of the proposed method
[234] |  Tiny-ViT |  P |  – |  – |  – |  98.35 |  92.97 |  Improve predict lymph node metastasis from WSIs efficiently using a novel transformer-guided.
[235] |  Convolutional |  S |  – |  – |  – |  – |  – |  The Unet architecture has been optimized to create improved cross-sectional images with clearer texture and higher quality
[236] |  T2T-ViT |  C |  86.60 |  – |  – |  – |  – |  The model parameters are reduced , and the model performance and computation are greatly improved compared with CNN.
[237] |  PyMLViT |  C |  87.50 |  – |  – |  – |  – |  Optimize the training process of the network

Abbreviations: segmentation (S), Classification (C), Prediction (P), Guided
multi-modal multi-instance learning (GMMMIL).

###  5.2 TC diagnosis example

To exemplify the approaches adopted in the literature for TCD and the
utilization of AI in classifying cancer types, we provide a simplified
example. The pattern recognition process entails training a neural network to
accurately classify input patterns into specific target classes. Following
training, the network becomes capable of categorizing model. In this part, we
demonstrate an example of categorizing TC into benign, malignant, or normal
based on a collection of characteristics according to the TIRADS.

In this case, we utilize a dataset obtained from the UCI ML Repository [238].
This dataset is instrumental in developing a neural network designed to
categorize patients visiting a clinic into three distinct groups: normal,
hyperfunctioning, or subnormally functioning. The dataset is structured into
Thyroid Targets (TT) and Thyroid Inputs (TI) as detailed below: (i) TI
consists of a 21×7,200 matrix that describes 7,200 patients through 15 binary
and 6 continuous attributes. (ii) TT is a 3×7,200 matrix with class vectors,
distributing each patient input into one of the three categories: (1)
Hyperfunctioning, (2) Normal, not suffering from hyperthyroidism, and (3)
Subnormal functioning.

In this neural network setup, the dataset is divided into 5,040 samples for
training, 1,080 samples for validation, and 1,080 samples for testing
purposes. The network undergoes training to minimize the error between the
thyroid inputs and targets until it achieves the desired target objective. If
the error rate (ER) fails to decrease and training progress stalls, training
with the training data is stopped, and the validation data is utilized for
additional evaluation. Subsequently, the testing data is employed to assess
the accuracy of the trained model.

In this illustration, we employ a network featuring 10 hidden layer neurons,
21 input features, and 3 output classes. Following the model simulation, the
percentage error is computed, yielding values of 5.337% for training, 7.407%
for validation, and 5.092% for testing. The overall recognition rate stands at
94.4%, with an overall error rate of 5.6%. The receiver operating
characteristic (ROC) curve is presented in Figure 10. This example showcases
the application of AI in TC classification, achieving a high recognition rate
with the provided dataset. Figure 11 illustrates an instance of thyroid
segmentation in US images utilizing k-means clustering, with three clusters
selected for demonstration. K-means clustering is widely employed for such
purposes.

Figure 10: An example of TC classification USING ROC metric. (a). Training
ROC; (b). Validation ROC; (c). Test ROC; (d). ALL ROC put the refernece,
mention first group what does it mean and the second group also, put (a) and
(b) for each group Figure 11:  Thyroid segmentation example employing the
K-Means method. (a) Imaging of medullary TC; (b) Ultrasound imaging of the
thyroid.

##  6 Critical analysis and discussion

Data Accessibility and Precision Challenges: This document necessitates a
thorough investigation and discussion on leveraging AI for thyroid carcinoma
detection. While there’s ample discourse regarding AI’s potential in existing
studies, a nuanced analysis is crucial to fully decipher its impact on
healthcare, spotlighting both its advantages and drawbacks.

This segment commits to an exhaustive examination of AI models’ proficiency in
identifying thyroid carcinoma. We aim to scrutinize not merely their
statistical precision but also their efficacy in practical clinical
environments, and their contribution to the overarching clinical decision-
making framework.

Furthermore, this exploration delves into the potential biases embedded in AI
models, seeking to unveil how they might inadvertently amplify existing
healthcare inequities. By contrasting AI-enabled methodologies with
conventional diagnostic tactics, we aspire to glean deeper insights into their
relative effectiveness.

Recognizing the obstacles in integrating AI solutions into healthcare
practices, including infrastructural, regulatory, and cultural challenges, is
essential. In conclusion, we highlight the critical role of cross-disciplinary
cooperation in seamlessly integrating AI into healthcare systems, thereby
maximizing its beneficial impacts on patient health outcomes.

The reported metrics of AI models, such as accuracy, sensitivity, and
specificity, can vary significantly across academic publications due to
factors like the choice of dataset, data quality, and the methodological
approach utilized. The performance of AI models in controlled experimental
setups may not accurately represent their effectiveness in actual clinical
scenarios. Variables including data discrepancies, lack of complete data, and
evolving clinical conditions can substantially influence outcomes. Therefore,
assessing a model’s flexibility and reliability in diverse conditions is
crucial. Table 7 provides an overview of the performance indicators for
various AI-enhanced TCD frameworks, presented in percentage (%) terms, across
multiple models and data sources. Additionally, Figures 12 and 13 display
these performance indicators, also in percentage (%) terms, with a focus on
private datasets and US imaging data, respectively.

Figure 12: Performance assessment of TC frameworks in percentages (%) for
private datasets  Figure 13: Performance assessment of TC frameworks in
percentages (%) for ultrasound. Table 7: Assessment of the effectiveness of
different TC frameworks, measured in percentages (%).

Ref. |  AI Model |  Dataset |  SEN |  SPE |  ACC |  AUC
---|---|---|---|---|---|---
[31] |  CNN |  DICOM |  - |  91.50 |  - |  -
[46] |  RF |  US |  - |  - |  - |  94.00
[102] |  ThyNet |  PD |  - |  - |  - |  92.10
[103] |  VGG-16 |  CI |  - |  - |  97.66 |  -
[120] |  Fine-Tuning DCNN |  PD |  - |  - |  99.10 |  -
[123] |  DCNN |  SGI |  93.00 |  86.00 |  89.00 |  -
[167] |  DNN |  ACR T |  - |  - |  87.20 |  -
[239] |  Ensemble DL |  CI |  - |  - |  99.71 |  -
[240] |  k-SVM |  US |  - |  - |  - |  95.00
[241] |  ANN |  US |  - |  - |  - |  69.00
[242] |  SVM RF |  US |  - |  - |  - |  95.10
[243] |  ANN SVM |  US |  - |  96.00 |  - |  -
[244] |  RF |  US |  - |  - |  - |  75.00
[245] |  CNN |  DICOM |  82.40 |  85.00 |  83.00 |  -
[246] |  ResNet18-based |  PD |  - |  - |  93.80 |  -
[247] |  multiple-scale CNN |  PD |  - |  - |  82.20 |  -
[248] |  Alexnet CNN |  PD |  - |  - |  86.00 |  -
[249] |  CNN (BETNET) |  US |  - |  98.30 |  - |  -
[250] |  ResNet |  T |  - |  75.00 |  - |  -
[251] |  Xception |  CT images |  86.00 |  92.00 |  89.00 |  -
[252] |  Google inception v3 |  HPI |  - |  - |  95.00 |  -
[253] |  CNN |  T |  81.80 |  86.10 |  85.10 |  -
[254] |  CNN |  T |  78.00 |  85.00 |  82.10 |  -
[19] |  CNN |  T |  80.60 |  80.10 |  80.30 |  -
[255] |  CNN |  MRI |  65.00 |  80.00 |  79.00 |  -
[256] |  CNN |  CT image |  93.00 |  73.00 |  84.00 |  -
[257] |  CNN |  US |  - |  - |  77.00 |  -

Abbreviations: Ultrasound (US), TIRADS (T), Cytological images (CI), Sono
graphic images (SGI), Histo pathology images (HPI)

Although AI methodologies have shown promise in TC diagnosis, they face
challenges that hinder the development of efficient solutions, lead to
increased expenses, and limit their broad application. For precise detection
of TC, it’s essential to collect and securely consolidate all relevant data in
a single repository, unless adopting federated learning (FL) approaches, as
Himeur et al. suggest [258]. Following this, algorithms capable of identifying
all forms of TC must be developed. Comprehensive TCD should include an
extensive array of training and testing images, diagrams of nodules, and
detailed classifications of nodule characteristics across different sizes, as
Shah et al. recommend [259]. It’s crucial for these datasets to be continually
updated with data from MRI, CT scans, X-rays, and other clinical images to
assess TC accurately. Inclusion of demographic details such as race,
ethnicity, gender, and age is also necessary. Establishing a centralized
database accessible to all healthcare facilities for testing, validating, and
implementing AI algorithms on the collected data is critical, following
Salazar et al.’s guidance [260]. Additionally, a succinct overview of further
limitations and challenges yet to be addressed is provided.

  * •

The challenge of obtaining clean data and ensuring accuracy: In the realm of
cancer diagnosis, especially TC, a major obstacle is the absence of detailed
and well-annotated datasets that comprehensively document the disease’s
incidence and progression. Elmore et al. [261] point out that the limited
detailed records on TC-related deaths make the process of collecting and
authenticating data particularly difficult. Data availability is often
restricted to individual healthcare providers, largely because there is no
consolidated clinical database for TC to support data exchange across multiple
organizations. Furthermore, the capability of AI algorithms to accurately
diagnose TC is hampered by the limited presence of labeled cases that
correlate with actual clinical outcomes, as underscored by Park et al. [262].
Although substantial datasets are essential for neural networks to generate
accurate results, there is a need for selective data incorporation during the
training phase to prevent the ingress of detrimental noise.

  * •

Thyroid gland imaging: Within the domain of TC diagnosis, imaging modalities
such as CT and MRI are available but often not preferred due to their high
costs and limited availability under certain circumstances, as highlighted by
Ha et al. [58]. US imaging is commonly used as an another physical exams
technique, fine-needle aspiration biopsies, or radioisotope scans for its
cost-effectiveness and accessibility. During an ultrasound examination,
medical professionals can evaluate the thyroid gland by analyzing the nodules’
echo patterns, along with their size, echogenicity, margins, and any
calcifications present. However, it’s crucial to acknowledge, as Zhu et al.
[263] have noted, that the accuracy of ultrasound in differentiating between
malignant and benign and nodules can vary, and the images obtained may be
prone to noise.

  * •

Hyperparameters of DL models: Designing the effective DL algorithm is crucial
for overcoming different challenges, especially in diagnosing TC. The task of
precisely differentiating between malignant and benign tumors, is complex due
to their significant similarities, as highlighted in the study by Wang et al.
[264]. Addressing this challenge may require significantly increasing the
number of DL layers for feature extraction. However, such an increase can lead
to longer processing times, particularly with large datasets, which may delay
timely cancer diagnoses for patients, as pointed out in the research by Lin et
al. [57].

  * •

Computation cost and storage limitations pose notable hurdles in algorithm
development. Time complexity, a key measure in algorithm evaluation, assesses
computational complexity by approximating the count of basic operations
performed and its relationship with the size of input data. Typically
represented as $O(n)$, where $n$ is the size of the input, often measured by
the bits required for its representation, this concept is thoroughly examined
in the study by Al et al. [265]. Particularly in AI research related to TC or
analogous cancer detection, researchers are tasked with finding algorithms
that offer a harmonious blend of accuracy and computational efficiency. Their
goal is to develop algorithms that can quickly process large datasets while
maintaining precise results. Furthermore, the extensive amount of data used in
these algorithms sometimes exceeds the storage capabilities, an issue
underscored in the research by Lin et al. [57].

  * •

Imbalanced dataset: Cancer element distribution within thyroid tissue cell
categories often shows considerable imbalance, with cancerous cells usually
forming a small fraction of the total tissue cell dataset. This results in a
dataset that is heavily skewed, containing both cancerous and normal cells.
Such imbalanced distribution of features in datasets for cancer cell detection
can cause less than ideal performance of AI algorithms used for detection
tasks, as observed in the study by Yao et al. [266].

  * •

Sparse labels:Annotation is crucial in the detection of CT, particularly in
differentiating normal cells from cancerous ones. However, this process can be
both time-consuming and costly, largely because of the limited availability of
annotated data. This shortage can result in inconsistent labeling, which may
negatively impact the precision of AI algorithms that depend heavily on
accurately labeled data. Consequently, these challenges may diminish trust and
confidence in AI applications, as discussed in the study by Yao et al. [266].

  * •

Presently, due to advancements in technology, especially in TC diagnosis, and
the growing availability of medical and patient data, researchers face
challenges in developing algorithms capable of efficiently handling a limited
number of samples. These samples could be characterized by noise, lack of
annotations, sparsity, incompleteness, or high dimensionality. As a result,
there is a need for AI algorithms that are not only highly efficient but also
able to process large datasets shared among healthcare providers and patients,
or between specialist doctors, as underlined in the research by Sayed et al.
[267].

  * •

Error Vulnerability: Despite AI’s inherent autonomy, it is prone to making
errors. For example, training an algorithm with TCDs for identifying cancerous
regions can lead to biased predictions if the training datasets are biased.
These biases may then lead to a series of erroneous results, which could go
unnoticed for a significant duration. Identifying and correcting the source of
these errors, once recognized, can be a laborious process, as explored in the
research by Karsa et al. [268].

  * •

The data form: Even with the significant advances in leveraging AI for TCD,
ongoing challenges due to several persistent limitations remain. The growing
need for varied medical imaging technologies results in the creation of large
datasets crucial for AI algorithms. Nonetheless, efficiently managing and
organizing this abundance of data has emerged as a daunting challenge. A
primary issue exacerbating this challenge is the absence of adequate labeling,
annotation, or segmentation of data, which complicates the process of data
management, as underscored in the research by Kim et al. [269].

  * •

Unexplainable AI: The application of AI in healthcare sometimes results in
”black box” outcomes, where the decision-making process lacks transparency and
sufficient justification. This lack of clarity can make healthcare
practitioners question the dependability of the results, possibly leading to
incorrect choices and treatments for patients with TC. In essence, AI systems
can operate as black boxes, providing outcomes without explicit and
comprehensible rationales, a concern highlighted in the research by Sardianos
et al. [270].

  * •

Lack of cancer detection platform: A significant barrier in detecting various
cancers, including TC, is the absence of platforms that facilitate the
replication and evaluation of previous research. This gap presents a
considerable challenge that hampers the assessment of AI algorithms’
performance, thereby hindering improvements [151]. The presence of online
platforms that offer cutting-edge algorithms, extensive datasets, and expert
insights is crucial for assisting healthcare practitioners, specialists,
researchers, and developers in making the right decisions with reduced chances
of error. Moreover, this kind of platforms are vital in augmenting clinical
diagnoses, as they enable more thorough Examination and assessment [271].

  * •

Data loss and contrast: The shift towards digital medical records is crucial,
notably in cancer diagnosis using slide images. This latter facilitate the use
of AI for pathologic examinations [272]. Nonetheless, medical digitalization
encounters specific challenges. There exists the danger of losing critical
information during the digital conversion process and potential inaccuracies
due to data compression methods applied in autoencoder algorithms. Thus,
selecting the right digitalization technology is essential to ensure the
preservation of data fidelity and authenticity [273, 274]. The subtle contrast
between the thyroid gland and surrounding tissues complicates accurate
analysis and diagnosis of TC.

##  7 Future research directions

This segment delves into the anticipated developments of AI in identifying TC,
scrutinizing forthcoming trends and advancements alongside their ethical
repercussions. Ethical concerns encompass more than the immediate area of
focus; issues regarding data privacy, responsibility, and fairness are also
discussed. This part underscores research avenues poised to significantly
improve TCD moving forward.

(a) Explainable Artificial Intelligence (XAI):  Integrating AI into decision-
making is crucial but faces challenges due to its complexity and lack of
clarity. To mitigate these issues, explainable artificial intelligence (XAI)
seeks to make AI models more transparent. This is particularly vital in
healthcare, where understanding the rationale behind AI-generated outcomes is
paramount. XAI has been applied to the diagnosis of intractable thyroid
diseases, as evidenced in studies by Lamy et al. (2019) [275], Kobylinska et
al. (2019) [276], Pintelas et al. (2020) [277], Lamy et al. (2020) [278], and
Poceviciute et al. (2020) [279]. The differentiation between standard AI and
XAI is showcased in Figure 14. Wildman et al. (2019) [163] proposed an XAI
approach for detecting TC, enhancing the confidence of healthcare
practitioners in AI predictions. XAI models clarify their reasoning,
addressing the limitations associated with opaque ”black box” algorithms. With
XAI, healthcare professionals are equipped to make more accurate and confident
decisions.

Figure 14: Diagram of XAI blocks.

(b) Using Cloud, fog, and edge computing:  The concept of edge networks merges
edge computing with AI, allowing AI algorithms to operate closer to where data
originates, a discussion brought forward by Sayed et al. (2023) [280]. This
method enhances efficiency and cost-effectiveness for data-intensive
applications, minimizing the requirement for extensive communication between
patients and healthcare providers. By positioning data and storage closer to
users in the healthcare field, this approach enables direct and swift access,
a point highlighted by Alsalemi et al. (2022) [281]. To further improve the
detection of TC within edge networks, fog computing is integrated. Fog
computing introduces a distributed framework that bridges cloud computing and
data generation sources, offering a versatile distribution of computational
and storage capacities at key locations to boost overall system performance
[282].

Cloud computing acts as a pivotal facilitator for the efficient functioning of
AI-driven TCD systems, offering readily available access to data storage,
servers, databases, networks, and applications for healthcare professionals,
contingent upon internet connectivity. This integrated approach has proven its
worth in medical scenarios, such as in the TC detection, as corroborated by
several researches [283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293].

(c) DRL:  reinforcement learning (RL), a branch of ML [294], enables agents to
navigate and make decisions in evolving environments by engaging in a learning
cycle of trial and error, observation, and interaction. The interest in
leveraging RL for diagnosing untreatable diseases and enhancing the support
for medical decision-making processes has grown recently. For example,
Balaprakash et al. (2019) [295] apply RL in cancer data classification,
whereas Li et al. (2020) [296] explore the use of deep RL for lymph node set
segmentation. In the approach by Li et al. (2020) [296], pseudo-ground truths
are created using RECIST-slices, facilitating the simultaneous tuning of lymph
node bounding boxes through the collaborative efforts of a segmentation
network and a policy network.

(d) DTL:  transfer learning (TL) is recognized as an effective approach to
reduce overfitting and improve the precision of diagnostic tools [297, 298,
299]. This technique applies knowledge acquired from one domain to solve
related issues in another, such as shortening the duration of training and
minimizing the amount of data needed [300, 267]. It is particularly useful in
diagnosing TG. For example, the Enhance-Net model, described in [301], could
act as a foundational model to boost the efficacy of a targeted DL model aimed
at analyzing medical images in real-time. Furthermore, in [150], the research
focuses on identifying pertinent characteristics of benign and malignant
nodules using CNNs. By transferring insights from generic data to a dataset of
US images, they achieve a fusion of hybrid semantic deep features. The
application of transfer learning has also proven beneficial in categorizing
images of thyroid nodules, as shown in [156]. Additional studies on this topic
include [302, 303, 304, 154, 305].

(e) PS:  Accurately identifying and segmenting objects with varied and
intersecting features continues to be a significant hurdle, especially in the
medical field. To tackle this issue, several scholars have developed holistic
and unified segmentation methods [306, 307]. Panoptic segmentation has
received considerable attention, merging the principles of instance and
semantic segmentation to detect and delineate objects efficiently. Semantic
segmentation involves the classification of each pixel into distinct
categories, whereas instance segmentation focuses on delineating individual
object instances. AI has been applied to this framework through either
supervised or unsupervised instance segmentation learning techniques, making
it highly applicable to medical scenarios. The effectiveness of this
integration in medical contexts has been highlighted in research works like
[308, 309].

(f) IoMIT and 3D-TCD: The internet of medical imaging thing (IoMIT) has gained
substantial interest in the healthcare industry in recent years. IoMIT seeks
to advance the quality of healthcare services and minimize treatment expenses
by facilitating the exchange of medical information between patients and
healthcare providers via interconnected devices equipped with wireless
communication technology. An instance of such integration is showcased in
[310], where an AI-enhanced solution for the preemptive identification of TC
within the IoMIT paradigm is introduced. This method employs CNN to refine the
distinction between benign and malignant nodules, aiming ultimately at life
preservation. Additional investigations pertinent to IoMIT include studies
like those found in [311] and [312].

2D ultrasound is a prevalent technique for evaluating thyroid nodules, yet its
static imagery might not fully capture the nodules’ complex structures.
Consequently, there’s a growing interest in utilizing three-dimensional (3D)
ultrasound, which offers a holistic view of the lesion by reconstructing
nodule characteristics, thereby facilitating enhanced discrimination between
different diagnostic categories [313]. The capability of 3D ultrasound to
analyze intricate growth patterns, edges, and forms from various perspectives
and depths allows for a more accurate assessment of thyroid nodules’
morphological features compared to 2D ultrasound. Comparative research has
confirmed the superiority of 3D over 2D ultrasound in achieving more precise
evaluations [314, 315, 316].

(g) AI-based thyroid surgical techniques:  As surgical practices face complex
challenges, the essential role of AI-driven robotic assistance is becoming
increasingly recognized. AI has the capability to navigate clinical
intricacies by processing and leveraging large volumes of data, offering
decision-making support with precision that rivals that of medical experts
[317]. Businesses are AI into surgical operations through the development of
AI systems and the deployment of robots to aid surgeons in the operating room.
These robots fulfill various functions, such as managing surgical tools,
handling potentially contaminated materials and medical waste, conducting
remote patient monitoring, and compiling patient information including
electronic health records, vital signs, lab results, and video documentation
[318]. It is therefore vital for surgeons to develop a comprehensive
understanding of AI and its potential impacts on healthcare. While AI-enabled
robotic surgery is still emerging, fostering interdisciplinary collaboration
can accelerate the progress of AI technology, thereby improving surgical
outcomes [319, 320, 321, 322] [323, 324, 325, 326].

Figure 15: Diagram of AI-algorithms choice for TCD.

(h) Recommender systems (RSs):  The vast amount of information produced by
online medical platforms and electronic health records presents a challenge
for TC patients seeking specific and accurate data [327]. Additionally, the
substantial costs associated with healthcare data management can complicate
the task for physicians handling a broad spectrum of patients and treatment
alternatives. The implementation of RS has been suggested as a solution to
improve decision-making within healthcare, reducing the load on both patients
and oncologists [328, 329]. Incorporating RS into digital health facilitates
tailored recommendations, precise evaluation of large data sets, and stronger
privacy measures, leveraging the capabilities of AI and ML technologies [330].

(i) FL: FL has gained traction in healthcare applications due to its capacity
to enhance patient data privacy across different healthcare settings [331,
258]. The influence of environmental factors on human health, which can
subsequently impact economic stability, is substantial. An increase in the
incidence of thyroid gland disorders has been observed across diverse
populations. ML plays a crucial role in addressing such health issues by
leveraging collected data to train models capable of foreseeing severe health
conditions. Considering the critical need for maintaining the confidentiality
of patient information among various health institutions, federated learning
(FL) stands out as an optimal framework for these purposes, as illustrated in
Figure 16. Lee et al. [332] conducted a study comparing the effectiveness of
FL with five traditional DL techniques (SE-ResNext50, SE-ResNet50, ResNext50,
ResNet50, VGG19) in analyzing and detecting TCD.

Figure 16: The FL for healthcare.

(j) Generative chatbots:

Recent breakthroughs in AI, notably in creating generative chatbots and
expansive language models like the GPT series, have marked a significant
stride forward [333]. These advanced models, trained on vast datasets, can
produce text that mimics human speech and engage in meaningful dialogues
beyond basic scripted responses. As these models have evolved, their
application spectrum has broadened, with healthcare becoming a key area of
application. In this sector, these models are being explored for patient
interaction, initial symptom evaluation, dissemination of health information,
and aiding healthcare workers in research and data analysis [4]. Integrating
such technologies promises to enhance healthcare operations, patient
experiences, and professional healthcare services, provided that necessary
safety measures and ethical guidelines are observed [334].

Applying generative chatbots or models like ChatGPT directly for diagnosing TC
(or any medical condition) would be imprudent and risky. Nevertheless, they
can play supportive roles within healthcare environments [335]. For example,
chatbots can initially collect information from patients about their symptoms,
familial health history, and lifestyle factors. This preliminary data
gathering can enrich the understanding of a patient’s situation before they
consult a healthcare practitioner. Additionally, chatbots can inform about TC,
including its risk factors, symptoms, and prevention measures [336],
empowering patients with knowledge about the condition and prompting those
with relevant symptoms to seek professional advice. Although not a substitute
for professional diagnostics, these tools can guide users through questions
that pinpoint potential risks or symptoms, encouraging them to seek further
evaluation from a healthcare professional [337].

After diagnosis, chatbots can inform patients about treatment options, side
effects, nutritional advice, and answer frequently asked questions. They can
also (i) remind patients about medication times, upcoming appointments, or
regular self-checks, (ii) support relaxation techniques, offer mental health
resources, or provide empathetic listening, and (iii) assist healthcare
workers by offering instant access to information on TC, latest research, or
treatment alternatives, serving as a real-time resource [338].

##  8 Conclusion

This investigation delves deeply into ML and DL, highlighting their growing
prominence due to their enhanced precision over other methods. It
comprehensively reviews various algorithms and training models, discussing
their benefits and drawbacks. Specifically, DL techniques are celebrated for
their application in a myriad of real-world scenarios, notably for their
generalization capabilities and resilience to noise.

Nevertheless, significant challenges obstruct the full adoption of DL in
detecting TC, with the lack of clean data and appropriate platforms being
primary concerns. Tackling these data challenges with detailed precision is
essential for creating effective and robust models for detecting more complex
cancer stages.

Future research should aim at overcoming these hurdles and improving TCD
methods. This study highlights the urgent need for increased research focus on
TC diagnostics to match the high precision expectations of healthcare
practitioners. While cancer detection in two or three dimensions is
progressing, the limited expertise in handling various geometric
transformations and multi-dimensional data compromises the accuracy in
diagnosing life-threatening diseases. Therefore, it is vital to innovate in
distinguishing between cancerous nodule sizes. Such innovations could
significantly speed up treatment, improve diagnostic precision, foster
proactive epidemiological tracking, and reduce death rates.

Novel technologies like Explainable AI, edge computing, reinforcement
learning, privacy-preserving mechanisms, and remote sensing are paving new
paths in TCD research. These developments are crucial for medical
professionals, simplifying the diagnostic process, reducing detection times,
and enhancing patient confidentiality. Future research will explore the impact
of these advanced technologies further. The objective is to create a major
transformation in cancer detection approaches by crafting advanced, privacy-
focused technologies for the identification of TC patients and extending into
domains like telehealth.

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