# Case Studies on X-Ray Imaging, MRI and Nuclear Imaging

Shuvra Sarker
Research and Development Department, Pioneer Alpha,
Dhaka, Bangladesh
<EMAIL_ADDRESS>
&Angona Biswas
Research and Development Department, Pioneer Alpha,
Dhaka, Bangladesh
<EMAIL_ADDRESS>
&MD Abdullah Al Nasim
Research and Development Department, Pioneer Alpha,
Dhaka, Bangladesh
<EMAIL_ADDRESS>
&Md Shahin Ali
Department of Biomedical Engineering, Islamic University, Kushtia-7003,
Bangladesh
<EMAIL_ADDRESS>
&Sai Puppala
Department of Computer Science, University of Alabama at Birmingham,Alabama,
USA
<EMAIL_ADDRESS>
&Sajedul Talukder
Department of Computer Science, University of Alabama at Birmingham,Alabama,
USA
<EMAIL_ADDRESS>
###### Abstract

The field of medical imaging is an essential aspect of the medical sciences,
involving various forms of radiation to capture images of the internal tissues
and organs of the body. These images provide vital information for clinical
diagnosis, and in this chapter, we will explore the use of X-ray, MRI, and
nuclear imaging in detecting severe illnesses. However, manual evaluation and
storage of these images can be a challenging and time-consuming process. To
address this issue, artificial intelligence (AI)-based techniques,
particularly deep learning (DL), have become increasingly popular for
systematic feature extraction and classification from imaging modalities,
thereby aiding doctors in making rapid and accurate diagnoses. In this review
study, we will focus on how AI-based approaches, particularly the use of
Convolutional Neural Networks (CNN), can assist in disease detection through
medical imaging technology. CNN is a commonly used approach for image analysis
due to its ability to extract features from raw input images, and as such,
will be the primary area of discussion in this study. Therefore, we have
considered CNN as our discussion area in this study to diagnose ailments using
medical imaging technology.

_K_ eywords Medical imaging, X-ray, MRI, Nuclear Imaging, Deep learning,
Diagnosis, Artificial intelligence

##  1 Introduction

As part of growing advancements in the medical field, Medical imaging played a
significant role in terms of diagnosing a variety of ailments. Medical Imaging
is the process of viewing and monitoring the interior of the anatomy that aids
in the diagnosis and treatment of disorders [1]. Some of the popular
techniques including X-ray, MRI, and Nuclear Imaging are used for diagnostic
procedures that provide images of the human interior organs and bones.

The X-ray (Rontgen radiation) is a non-invasive clinical diagnostic technology
that yields 2D images of the internal structure of the body by utilizing
electromagnetic radiation of extremely high frequency, high energy, and short
wavelength [2]. It was invented by Wilhelm Conrad Rontgen, a German scientist,
who named it x-radiation due to its unknown type of nature [4]. Rontgen
discovered that invisible rays from a cathode-ray tube were penetrating
through cardboard, causing the fluorescent screen to illuminate. An X-ray beam
is carried through the anatomy, with some of the radiation being absorbed or
deflected by the interior while the remainder is delivered to a detector
(photographic film), resulting in images of the internal structure [6]. Less
dense tissues appear as gray on the detector as most of the X-rays can pass
through the soft tissue, whereas densely packed structures appear as white,
e.g., bone. In addition to detecting fractures (broken bones), X-rays can also
be utilized to inspect the kidney, bladder, lungs, stomach, and liver [7].
This helpful feature from X-ray imaging assists doctors in further looking
into problems related to bones, joints, soft tissues, and other sections of
the body effectively. Moreover, guiding surgeons during coronary angiograms
for emergency treatment after a heart attack is another vital role of X-rays.
They can also be used to treat cancer, pneumonia, injuries, breast cancer, and
dental issues. Recently, the diagnosis of bacterial pneumonia and the most
serious life-threatening COVID-19 disease has relied heavily on X-ray imaging.
Several diseases can be diagnosed effectively by employing X-ray images. Among
them, several studies have been carried out on COVID-19. Identifying the
importance of this topic we have discussed in detail in this chapter about
detecting some of the issues related to COVID-19 from x-ray images. Since
COVID-19 is a pandemic disease and quick treatment could help in the early
identification of bodily issues, Artificial Intelligence (AI) along with this
radiological x-ray imaging will assist in diagnosing the disease. The majority
of the studies are centered on deep learning, which can diagnose illnesses
autonomously through X-ray images.

Another non-invasive medical imaging approach is Magnetic Resonance Imaging
(MRI) which yields 3-D detailed, high-quality images of the internal structure
of the body from various angles. Although the invention of MRI is done by
Raymond Damadian, the Nobel Prize was awarded to Paul Lauterbur and Peter
Mansfield for providing a basis for gathering information from high-quality
images [8]. MRI scanners are well suited to capture images of the soft tissues
of the anatomy. MRI can be employed to examine the blood vessels and heart,
internal organs (liver, kidneys, etc.), cancers, and other structures [9].
This technology uses radio frequency (RF) and a strong magnetic field instead
of damaged electromagnetic radiation. After aligning the protons in the human
body a radio frequency is provided which causes the input proton stream to
become misaligned. When the RF is shut off, the protons realign, but they
continue to send radio signals back, permitting the scanner to gather images
of a specific region of the body. MRI is a prominent screening test for the
diagnosis of any type of brain or spinal cord disorder. Additionally, AI can
evaluate the image for any form of diagnosis in order to gain new discernment
about any ailment. MRI has recently become the most effective way to detect
brain tumors in the medical field. According to prior research, For automating
the identification of this brain tumor using MRI, AI-based approaches yield
the most accurate and efficient results.

Nuclear medicine imaging is quite distinct from other conventional imaging
modalities. It is a process of tracking radiation released by the body as
opposed to radiation produced by other external sources, such as X-rays. It
differs from other radiological imaging since it emphasizes the organ’s
functionality, whereas other methods focus simply on the organ’s appearance.
It makes use of radioactive substances (radiopharmaceuticals), which are
ingested or inhaled into the body, and the detector records images from the
radiation released from the radiopharmaceuticals. It focuses on organ tissue,
such as lung scans, brain scans, heart scans, bone scans, gallbladder scans,
and tumors [10]. This approach may also identify any damage and target the
damaged cell for destruction by a tracer to halt the development of that
damaged cell. In this chapter, some of the use cases of medical imaging
modalities are briefly explored, with an emphasis on their applicability to
the most frequent ailments. This study presents the diagnosis of some
particular diseases utilizing medical imaging with the help of AI
technologies. This study offers a thorough review of illness diagnosis using
X-ray, MRI, and NI with the use of DL techniques studied in numerous research
publications. Recently, DNN has achieved remarkable traction in the realm of
the medical field for its autodidact capacity to classify disorders. The brief
review of the existing methods will assist researchers in obtaining knowledge
in this field and conducting a further study on this issue.

An insightful analysis of X-ray, MRI, and NI imaging modalities approaches and
their applications are presented in this chapter after reviewing numerous
articles. Based on the prior research from several articles, only a few are
selected to study the method they used in their article along with the result
they achieved, which are presented in brief here covering x-ray, MRI, and
Nuclear imaging (NI).

The below content is organized in the following structure: The related studies
on the mentioned topic are presented briefly in section 2. In Section 3,
materials and methodology are described. In Section 4, the results and
analysis are depicted. Section 5 presents the main findings of this chapter.

##  2 Background Study and Related works

###  2.1 Medical Imaging and Essential Study in Medical Science

####  2.1.1 Medical Imaging

The history of medical imaging dates back to the discovery of X-rays by
Wilhelm Conrad Roentgen in 1895. Since then, the field of medical imaging has
evolved rapidly with the development of various imaging modalities, such as
computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, and
positron emission tomography (PET). Here is a brief overview of the major
milestones in the history of medical imaging: In 1895, Roentgen discovered
X-rays, a form of electromagnetic radiation that can penetrate through the
body and produce images of internal structures. X-rays quickly became a
popular diagnostic tool for detecting fractures, tumors, and other
abnormalities. In the 1950s, ultrasound imaging was developed, which uses
high-frequency sound waves to produce images of internal structures.
Ultrasound is particularly useful for imaging soft tissues and organs, such as
the liver, kidneys, and fetus during pregnancy. In the 1970s, the first CT
scanner was developed, which uses X-rays and computer processing to produce
detailed cross-sectional images of the body. CT scans are particularly useful
for detecting tumors, injuries, and internal bleeding. In the 1980s, MRI was
developed, which uses a powerful magnetic field and radio waves to produce
detailed images of the body. MRI is particularly useful for imaging soft
tissues, such as the brain, spinal cord, and joints. In the 1990s, PET
scanning was developed, which uses a radioactive tracer to produce images of
the body’s metabolic activity. PET scans are particularly useful for detecting
cancer, heart disease, and neurological disorders. In conclusion, medical
imaging has come a long way since the discovery of X-rays in 1895. The
development of new imaging modalities and technologies has revolutionized the
field of medicine, enabling doctors to diagnose and treat diseases with
greater accuracy and precision.

####  2.1.2 X-Ray in Medical Science

X-rays are commonly used to diagnose and monitor bone fractures, tumors,
infections, and other abnormalities in the body. X-ray machines produce a
controlled amount of radiation that passes through the body and creates an
image on a film or digital detector. The resulting image shows the internal
structures of the body in shades of gray, with denser structures appearing
whiter and less dense structures appearing darker.

X-rays are a type of electromagnetic radiation that has a wavelength shorter
than visible light. When X-rays pass through the human body, they are absorbed
in different rates by different tissues and structures, depending on their
density and composition. This property of X-rays makes them a useful tool for
medical imaging, allowing doctors to visualize internal structures and
diagnose various medical conditions.

####  2.1.3 MRI in Medical Science

MRI technique is particularly useful for imaging soft tissues, such as the
brain, spinal cord, and organs, which are difficult to see with other imaging
techniques. MRI can detect a wide range of medical conditions, including
tumors, infections, injuries, and neurological disorders. It can also be used
to monitor the progression of certain diseases and to guide surgical
procedures.

MRI, which stands for Magnetic Resonance Imaging, is a medical imaging
technique that uses a strong magnetic field, radio waves, and a computer to
create detailed images of the body’s internal structures. Unlike X-rays, MRI
does not use ionizing radiation and is considered safe for most patients [43].

The procedure of MRI conduction in medical science: the patient lies down on a
table that slides into a large tube-shaped machine. The machine creates a
powerful magnetic field that aligns the hydrogen atoms in the patient’s body.
Radio waves are then used to disrupt the alignment of these atoms, causing
them to emit signals that are detected by the machine’s receiver. These
signals are then processed by a computer to produce detailed images of the
body’s internal structures.

####  2.1.4 Nuclear imaging in Medical Science

Nuclear imaging, also known as nuclear medicine imaging, is a medical imaging
technique that uses small amounts of radioactive materials, called
radiopharmaceuticals, to create images of the body’s internal structures and
functions. Nuclear imaging is different from other medical imaging techniques
because it can show how different parts of the body are functioning rather
than just their structure [55].

During a nuclear imaging procedure, a small amount of radiopharmaceutical is
injected into the patient’s body, usually into a vein. The radiopharmaceutical
travels through the patient’s body and accumulates in the organ or tissue
being studied. The patient then lies down on a table and a special camera is
used to detect the radiation emitted by the radiopharmaceutical. This
radiation is then processed by a computer to create images of the body’s
internal structures and functions.

Several works have been conducted based on the nuclear medical imaging
technique. Firstly, we consider X-ray images for depiction. For better
analyzing the x-ray imaging modality, detecting coronavirus from the x-ray
image has been considered as an example due to its easily accessible quality.
It has been considered the primary diagnostic imaging tool for the
Coronavirus. There are several pieces of research on this that permits us to
take it as an example. The automated identification of this disease is a
source of concern due to its infectious nature and the scarce number of test
equipment accessible. As a result, previous research relied on an AI-based
technique. Recent advancements in deep learning assist in interpreting X-ray
images. Among the prior studies, the most frequent and effective method is
utilizing CNN through the X-ray images, as it can differentiate features from
other common pneumonia directly from raw data. X-ray images is playing a very
crucial role in the ongoing pandemic for detecting any lung abnormalities in a
timely manner.

Transfer learning technique was adopted along with CNN to recognize COVID-19
from x-ray images, which achieved a tremendous result [11]. The key emerging
tool is an x-ray, from which the data were obtained in order to detect and
categorize whether the patient has COVID-19 or not. Ozturk et al. [12]
proposed a DL-based model named ‘DarkNet’ as a classifier where 17
convolutional layers were used along with different filtering from x-ray
images to automatically identify COVID-19 to diagnose for binary class
categorization (accuracy of 98.08%) and multi-class categorization (accuracy
of 87.02%). In [13], COVIDX-NET, a DL classifier, was proposed that was
trained by seven distinct structures of DCNN models to interpret patient
status with 50 x-ray images. The efficacy of MobileNetV2, one of the
classifiers, can be enhanced further for use on smart devices due to its fast
computing speed in the clinical field. Narin et al. [2] proposed five pre-
trained CNN-based approaches utilizing X-ray images, and they found that the
pre-trained ResNet50 model offers the highest accuracy. In that model, three
distinct binary classifications were implemented with four classes. Wang et
al. [14] presented COVID-Net, a deep CNN-based design tool that is a public
source and accessible to all for making decisions in diagnosis. A sufficient
number of datasets should be necessary to accurately identify and categorize
the disease.In [68] COVID-Net therefore plays a crucial role in providing
sufficient and current data for automatic classification. They proposed a
method for creating this dataset, which will aid radiologists in correctly
interpreting X-ray images.

MRI employs an intense magnetic field combined with radio frequency to offer
comprehensive information about soft tissues, bones, organs, and other body
interiors. As it provides a detailed picture of soft tissue with 3-D
visualization, the detection of brain tumors has been selected as an example
from MRI. High precision is needed for identifying brain tumors since even a
small inaccuracy might have fatal consequences. The detailed image from MRI
can assist the physiologist in determining the particular disease in a
particular area due to its capacity to discern between structure and tissue
focused on contrast levels. Several studies have been carried out on the
automatic detection of brain tumors. In [15], the authors proposed an
automated brain tumor identification technique based on datasets that have
been pre-processed with a diffusion filter. The median filter as well as SWT
were employed to de-noise it. Following that, segmentation was conducted, and
SWT was utilized to extract features before segmenting and classifying with
SVM or PNN. In [16], Saladi et al. proposed a more accurate and precise brain
tumor detection method for segmentation purposes that makes use of versatile
regularized kernel-based fuzzy c-means (ARKFCM). The removal of noise and
segmentation present the most challenging issues for MRI brain tumor
diagnosis. Three distinct enhancement approaches, notably HE, CLAHE, and
BPDFHE, were employed for preprocessing to remove noise from the MRI image. In
[17], three distinct types of tumors were identified utilizing K-means for
segmentation, and features were extracted through DWT and then the feature was
reduced by adopting PCA. Finally, ANN was adopted for classification. An
efficient training function “Levenberg-Marquardt” was employed in the
presented network design. To identify tumors and their location, a quicker
R-CNN DL technique presented (RPN) with Region Proposal Network. VGG-16 was
adopted as a formation structure in that model [18]. The binary classification
was described in [19], where VGG-16 and AlexNet were utilized for extracting
features. They aimed to first increase the remarkable qualities utilizing the
hypercolumn strategy, and then merge the information acquired using both
structures. The fittest features are chosen to employ recurrent feature
elimination (RFE). Afterward, Support Vector Machine (SVM) was employed for
categorization, yielding an overall accuracy of 96.77 %. In [56], the authors
presented a DWT-based CNN model in which four MRI sequences were fused via DWT
with DW kernel, yielding a more comprehensive tumor region than a single MRI
sequence. Thereafter, a partial differential diffusion filter (PDDF) was
employed for noise elimination and then segmentation was done utilizing a
global thresholding approach. The segmented images were then fed into the
23-layer CNN architecture for classification purposes.

A retrospective analysis was carried out to diagnose lung cancer utilizing
nuclear imaging. In [21], SPECT as well as PET(positron emission tomography)
were integrated with CT to diagnose lung cancer with 3D treatment planning.
They examined the nuclear imaging technique with CT images to improve the
accuracy. PET is frequently used in brain tumor analysis. This technique can
distinguish between benign and malignant brain tumors. Information from
functional evaluations of each organ is a subject of concern for benign
lesions, letting the physician know whether the organ is normally activated or
not [22]. In [23], the authors evaluated voxel-based criteria in conjunction
with dual-time-point 18F-FDG PET for the detection and localization of high-
grade malignant tumors and they improved sensitivity compared to standard FDG
PET. In [24], the authors proposed a technique for identifying brain tumors
utilizing carbon-11 choline along with PET and examined its effectiveness.
C-11 was administered intravenously to humans and rats with tumors. The
tracer’s dispersion throughout the brain was assessed. FDG, the best tracer in
PET, cannot differentiate between HG tumor and necrosis radiation [25].
Furthermore, PET scanning might provoke allergic reactions in individuals,
which can be harmful. Additionally, due to their inferior spatial resolution
compared to MRI scanning, PET tracers are unable to offer reliable
localization of physiological structure [26]. The authors presented a DL-based
approach and examined an approach using conventional CT images and CT images
derived from PET. ResNet-18 structure was utilized for pre-processing and
transfer learning was then used for classification. They demonstrated that
traditional CT provides greater accuracy than CT with PET [27]. In [28],
detection of coronary artery disease (CAD) was diagnosed utilizing SPECT
myocardial perfusion imaging (MPI). A robust CNN structure was employed for
feature extraction and further classification to identify MPI images. They
approached the RGB-CNN model which can train a model efficiently with a
limited dataset. The authors implemented another RGB-CNN model to categorize
SPECT data. They proposed three CNN approaches where RGB-CNN was designed from
scratch while VGG-16 and DenseNet-121 were implemented employing transfer
learning [29]. They exaggerated numerous CNN layers to obtain the best
combination with 10-fold cross-validation which resulted in high accuracy.
They compared their RGB-CNN method with VGG-16 and DenseNet-121 where RGB
demonstrates adequate performance with strong findings, VGG-16 functioned
effectively, and DenesNet-121 retrieved adequate data.

A lot of research advancements were made in X-ray, MRI, and NI Imaging
modalities. We have analyzed a few of the important studies in this chapter.
Also, In recent times we also reviewed significant traction towards federated
learning. federated learning provides an efficient way of detecting newly
discovered variants like COVID-19. Some of the useful findings about federated
learning are mentioned in [63] [64] [65]. In [66] a collaborative federated
learning system that enables deep-learning image analysis and classifying
diabetic retinopathy without transferring patient data between healthcare
organizations has been introduced.

##  3 Materials and Methodology of study

###  3.1 X-ray

X-ray imaging is the most effective diagnostic tool for identifying serious
illnesses such as pneumonia. Recently, this imaging technique has also been
utilized to diagnose the highly contagious and severe pandemic caused by the
coronavirus. Due to the urgency and rapid spread of this pandemic, early
detection is crucial in reducing mortality rates and slowing its transmission.
However, diagnosing this disease through X-ray images requires specialized
expertise or experienced individuals. Therefore, artificial intelligence (AI)
has emerged as the fastest and most efficient approach for automatically
detecting the coronavirus in X-ray images. Numerous studies have investigated
the use of X-ray scans in diagnosing this pandemic disease, and in this
context, we will provide specific examples to highlight the significance of
this imaging modality in determining the severity of this condition. Among
various AI techniques, this review will primarily focus on the CNN-based deep
learning approach due to its superior effectiveness [3].

####  3.1.1 Materials:

The progress impact of DL in medical imaging modalities relies on the text-
mined image dataset. A vast variety of existing methods have been studied
using X-ray imaging for observing various ailments, particularly lung
diseases. The detection of COVID-19 has been taken as an example using X-ray
images. A dataset is a primary requisite for diagnosing any disease. The chest
X-ray scans are obtained from distinct sources. Cohen JP’s dataset (covid-
chest x-ray-dataset) [30] is one of the frequently used datasets for amassing
X-ray images from patients having COVID-19, which considers images from
numerous free sources that are periodically updated. “ChestX-ray8” is another
database that contains 108,948 anterior-view X-ray images of 32,717 distinct
patients with annotated eight illness image descriptors that assist in
detecting thoracic disease [31].

Figure 1: Sample images of chest x-ray (A) COVID-19 case (B) Normal case (C)
viral pneumonia case.[14]

Sample images of the X-ray are shown in Figure 1. This figure depicts three
cases of X-ray images. The datasets consist of x-ray images(chest, lungs,
bones, and other organs) for recognition and classification purposes.

####  3.1.2  Methodology:

The COVID-19 virus can cause irreversible lung damage, resulting in pneumonia
and perhaps death. Therefore, it is very crucial for doctors to detect corona
at a very early stage to reduce mortality. Methods from several studies are
presented in this study. The authors used the fuzzy color technique to reform
the data classes and stack images that were organized using DL models
MobileNetV2 and SqueezeNet [32]. The models adopted the Social Mimic
optimization (SMO) approach to process the features. Following that, SVM was
utilized to aggregate and categorize efficient features. For the first time,
Joseph Paul Cohen explored the COVID-19 datasets and publicly shared them on
GitHub. The majority of the study for COVID-19 detection is based on this
dataset, which aids researchers in their COVID-19 categorization studies. In
their research, three different datasets were collected and the images were
pre-processed utilizing the Fuzzy technique. The datasets were trained through
DL models MobileNetV2 and SqueezeNet and classified using the SVM technique.
For image classification, CNN plays a vital role. As a result, the majority of
the works are based on CNN-based imaging modalities.

###  3.2 MRI

####  3.2.1 Materials:

MRI is the most effective and reliable screening technology for detecting
brain tumors (BT). It is the best technology that is frequently used as it can
diagnose BT at a very early stage. A brain tumor is a source of concern as it
is one of the prominent causes of death. As brain tumors do not disseminate to
other areas of the body, they can be distinguished from malignant tumors among
the numerous types of tumors. Among the three different variants of BT,
gliomas form in brain tissues and are considered malignant. Meningiomas
develop from the membranes that are benign, while pituitary tumors are masses
that rest inside the skull [33].

.

Figure 2: Image of MRI brain images [33]

Figure 2 depicts an image of a brain tumor from an MRI. These images are
obtained from the datasets for classification purposes or further analysis.

Several public datasets are made available for training the DL models, as the
models necessitate an ample amount of data. Three distinct commonly used
datasets are utilized in [34] which were preprocessed and later segmented
utilizing Fuzzy-c- means. To categorize BT images, the detection performance
of several classification techniques, including discriminant analyzer (DA),
K-nearest neighbor (KNN), SVM, decision tree (DT), neural network (NN), and
Naive Bays’ (NB), had been examined. In [35], the authors developed six
distinct CNN models utilizing BraTS 2013 dataset, which was later tested using
the WBA dataset. The best result was obtained for the KNN classifier employing
the OASIS dataset. Some available datasets from MRI images are depicted in
Table 1

Table 1: Table of some available datasets from MRI images . Reference | Datasets
---|---
Sahoo et al. [34], Kalaiselvi et al. [35], [36],[37] | BRATS
[38] | OASIS
[39] | IBSR
[40] | Kaggle
[41] | The Whole Brain Atlas (WBA)

####  3.2.2 Methodology:

The existing approaches employed for brain tumor identification require some
common procedures for precise classification. First of all, input images are
required for diagnosis and classification which are obtained from the MRI
image dataset Nasim et al. The images are then pre-processed through several
techniques such as high pass filter, low pass filter, or other filters for
eliminating noise. Then data are augmented for training purposes through DL
structure and then segmentation is performed. Image processing, thresholding,
etc. are done in this segmentation step to find the location of the tumor or
other specific tissue. After segmenting the particular area, this is used as
input to the DL structure. The preprocessed images can be passed through the
DL structure without segmentation. After that, the classification of distinct
tumors is performed utilizing several DL approaches such as CNN, ResNet,
GoogleNet, and so on, which learn and extract features automatically directly
from raw data. CNN is capable of learning patterns from raw images and
extracting features automatically. Transfer learning along with DL approaches
were used for classification purposes. In [42], the authors proposed tumor
classification where a pre-trained CNN model, VGG-19 was utilized for
extracting features, and then KNN was applied for the classification of BT on
two autonomous datasets BRATS 20018 as well as CE-MRI. In [44], the authors
proposed a more accurate detection procedure for BT utilizing Fuzzy c-means
(FCM) clustering with the help of two optimization tools. They suggested two
approaches and compared them to determine which was superior. FCM utilizing GA
(Genetic Algorithm) method was more time-consuming than the other. As
execution is an important parameter to analyze images, their proposed method
of PSO (Particle Swarm Optimization) with FCM took less time than the other.
Segmentation is a critical step in finding ambiguous regions from complicated
medical images. They presented segmentation approaches that made use of the
image processing technique FCM together with the different pre-processing
tools GA and PSO. The authors proposed a new approach for data augmentation on
numerous datasets. Several sets of MRI data were trained to utilize GAN
(generative adversarial net) to create MRI-like visuals.

###  3.3 Nuclear imaging

Nuclear imaging is an organ-based functional modality that has a significant
effect on patient care by offering tools for disease staging. It is an
approach for creating images by sensing radiation from distinct locations of
the anatomy after a radioactive tracer has been administered to the patient.
The benefit of evaluating an organ’s function is that it assists clinicians in
making plans and developing diagnostic strategies for the area of the body
being analyzed. This differs from other imaging modalities in that it employs
a tracer with no side effects and minimal radiation. Hence it is safer than
the other modalities. Furthermore, it provides some essential information at a
preliminary phase that other imaging modalities can’t provide. Following the
administration of the tracer and the accumulation of the tracer in the bodily
tissue being studied, radiation will be produced and detected by a gamma
camera. During a nuclear scan, physicians can analyze and diagnose disorders
such as tumors, infections, cysts, etc. by observing the behavior of
radioactive substances. The region of the body where the radionuclide
accumulates in larger proportions is called hot spot [45]. This modality can
detect abnormal renal function, brain, thyroid, and heart function, among
other things. Figure 3 presents an image from nuclear imaging technology.

Figure 3: Image of nuclear medicine imaging [46]

Nuclear imaging approaches are of two types-SPECT (single-photon emission
computed tomography) and PET (positron emission tomography) which yield
metabolic and functional details, as opposed to CT and MRI. They are
integrated with CT and MRI to disseminate necessary information and permit
interconnection between functional and physiological data. Compared to SPECT,
PET offers superior contrast and positional accuracy [47]. PET makes use of a
positron-emitting tracer, while SPECT employs a gamma-emitting tracer.

SPECT is a 3D imaging modality that enables radiopharmaceutical dispersion as
well as provides more clarity, brightness, and spatial information compared to
planar nuclear imaging. It employs numerous gamma cameras that spin around the
patient to provide greater accuracy and spatial precision. The reconstructed
data produces 3D images from cameras [48].

PET is used to assess radioactivity in vivo. It entails injecting a positron-
emitting tracer intravenously, allowing for distribution systemically, and
afterward monitoring for identification and measurement of radiopharmaceutical
dissemination shapes in the anatomy. F-18 fluorodeoxyglucose (FDG) is used as
a radiopharmaceutical in the cell. Having high metabolism, tumor cells
metabolize FDG which is processed into FDG-6 phosphate [47]. Tumor cells are
unable to consume that compound which is later aggregated and concentrated in
the tumor cells that fulfill the purpose of detection. Maps of the lung
perfusion in 3D are produced using SPECT. Tumor localization is aided by the
3D metabolic pictures produced by PET [67].

A DL-based approach to detect coronary artery disease utilizing SPECT, a
nuclear imaging (NI) technique has been taken as an example to show the
methodology from the NI technique. Most of the datasets were prepared by the
Nuclear Diagnostic Department to diagnose diseases from NI. The dataset for
this approach was prepared from a medical center where SPECT images were used
to visualize the heart. In [29], the authors proposed an RGB-CNN model where
the SPECT MPI images were first loaded and the images are presented in RGB
mode. Following that, the data was prepared through normalization, shuffling,
and splitting. The data augmentation was performed and the suggested CNN model
was trained. Data augmentation was done due to broadening the limited size of
the dataset. Their proposed model was able to identify three classes of heart
disease autonomously. They utilized appropriate parameters in their model to
correctly classify the cardiac disease automatically and examined using
different convolution layers for the best combination from where the
16-32-64-128 combination outperformed.

##  4 Result and analysis

A review of some existing methods has been presented here.

Table 2: Performance evaluation of numerous studies for COVID-19 detection from x-ray images: Authors |  Method |  Dataset |  Classes |  No. of X-ray samples |  Accuracy (%)
---|---|---|---|---|---
Nayak et al [50] |  ResNet-34 |  • covid-chestxray-dataset; • ChestX-ray8 |  2 |  406 • Covid19-203 • Normal-203 |  98.33 %
Ucar and Korkmaj [51] |  Bayes-SqueezeNet |  • covid-chest x-ray-dataset; • Kaggle chest X-ray pneumonia dataset |  2 |  5949 • COVID-76 • Normal-1583 • Pneumonia -4290 |  98.3%
Ozturk et al. [12] |  DarkCovidNet |  •covid-chestxray-dataset; • ChestX-ray8 |  Binary class (2) |  625 •COVID -125 •Normal-500 |  98.08%
Toğaçar et al.[32] |  SqueezeNet, MobileNetV2, SVM, SMO |  Stacked dataset of covid-chestxray-dataset and COVID-19-Radioraphy Dataset (kaggle) [52] |  3 |  458 COVID: 295, NORMAL: 65 and PNEUMONIA: 98 |  98.25%
Hemdan et al.[13] |  COVIDX-Net |  Cohen’s covid-chest x-ray-dataset |  2 |  50 COVID: 25; NORMAL: 25 |  90.00
Narin et al. [2] |  ResNet-50 |  Cohen’s covid-chest x-ray-dataset |  2 |  100 COVID: 50; NORMAL: 50 |  Dataset-1: 96.1% Dataset 2: 99.5% Dataset 3: 99.7%
Wang et al.[14] |  COVID-Net |  COVIDX |  3 |  13975 |  93.3%
Sethy and Behera [53] |  SVM, ResNet-50 |  Dataset from GitHub, Open-I and Kaggle |  2 |  50 COVID: 25; Normal: 25 |  95.38%
Farooq, Hafeez and Nasim [54, 68] |  COVID-ResNet |  COVIDx |  4 |  5941 |  96.23%

From Table 2, it is observed that numerous studies have been conducted
utilizing numerous methods. Among them, the ResNet-34 model achieves 98.33 %
for the detection of COVID-19 disease. X-rays can be utilized to diagnose
several diseases, such as heart disease, any type of fracture, lung disease,
and so on. The diagnosis of COVID-19 was chosen here as an example utilizing
the X-ray images. It is also assumed that the majority of the works used the
COVID chest X-ray dataset.

Table 3 shows that MRI images were utilized to recognize the most common brain
tumor. CNN model obtained better results using BRATS datasets.

The nuclear imaging technology was used to diagnose various diseases. Table 4
depicts several studies of this imaging technique for coronary disease
diagnosis. In that case, the RGB-CNN model performed better.

In light of the foregoing, it is determined that the majority of imaging
modalities employ convolutional neural network (CNN)-based deep learning
technology. It is frequently utilized for its best recognition and
classification purposes in images. It can classify images as input by
discovering patterns on its own. Therefore, it shows remarkable results for
image classification. Hence, medical imaging modalities rely on this CNN-based
technology for its automatic feature extraction and classification nature.

Table 3: Performance evaluation of numerous studies for brain tumor detection from MRI images: Authors |  Purpose |  Method |  Dataset |  Accuracy (%)
---|---|---|---|---
Kalaivelsi et al. [35] |  Detection along with Classification |  CNN |  BRATS 2013 dataset;The Whole Brain Atlas (WBA) |  96-99 %
Gopal et al. [44] |  Detection, segmentation, and classification |  Fuzzy c-means algorithm along with PSO and GA |  — |  GA with FCM:74.6% PSO with FCM:92.3%
Togaçar et al. [19] |  Binary classification |  CNN(AlexNet and VGG-16)for feature extraction, RFE for enhancing image and SVM for classification |  Dataset of Chakrabarty 2019(Kaggle) |  96.77%
Amin et al. [56] |  Binary Classification |  Conventional optimized filter used for pre-processing; Seed growing technique for segmentation; Stacked Sparse Auto-encoder(SSAE) for classification |  Multiple BRATS Challenge dataset |  100% on 2012, 90% on 2012 synthetic, 95% on 2013, 100% on Leaderboard 2013, 97% in 2014, 95% on 2015
Amin et al. [19][56] |  Classification |  DWT with DW kernel; CNN for classification |  BRATS dataset |  Average 97.6 %
Ghassemi et al. [57] |  Classify three distinct tumor classes |  GAN with ConvNet GAN to produce MRI-like visuals; CNN for classification |  Dataset by cheng et al[55], brain volume MRI images dataset [59] |  93.01%(introduced split) 95.6%(random split)
Salma et al. [60] |  Detection and Classification |  optimal wavelet statistical texture features(OGSA) along with (RNN) |  Images collected from star diagnostic and we resources |  96.26%
Table 4: Performance evaluation of numerous studies for coronary artery disease detection from Nuclear imaging technology: Authors |  Method |  Purpose |  Accuracy (%)
---|---|---|---
Papandrianos et al. [29] |  RGB-CNN |  Three class classification to diagnose CAD |  91.86%
Papandrianos and Papageorgiou [28] |  RGB-CNN |  Two class classification of CAD (ischemic or infarction) |  94%
Arsanjani et al.[61] |  ML based LogitBoost algorithm |  Early revascularization |  -
Berkaya et al. [62] |  Two classification techniques employing transfer learning and SVM |  Two class categorization of myocardial anomalies |  94%

##  5 Conclusion

This chapter represents several studies on three medical imaging modalities
X-ray, MRI, and nuclear imaging for diagnosis purposes. The findings of these
studies highlight the superiority of artificial intelligence (AI) approaches
in the field of medical science. DL-based models, particularly CNN-based
models, are widely used for image classification, eliminating the need for
manual feature extraction. This advanced structure can extract features
directly from raw data and can be constructed by combining different layers.
Although these DL techniques primarily require large datasets, data
augmentation can be used to benefit smaller datasets. The images from the
dataset undergo pre-processing through various filters, segmentation, and
classification, utilizing DL structures with precise features. The automatic
recognition and classification abilities of DL techniques from raw data
alleviate the workload of doctors and clinical staff.

From the reviewed studies, it is observed that X-ray is the most frequently
used technology in the diagnosis of lung diseases. Currently, the ongoing
pandemic COVID-19 is detected and classified using X-ray images, which have
achieved remarkable success using the DL technique. From the prior studies, it
is depicted that the ResNet-50 model achieves a high degree of accuracy in
determining COVID-19 disease. According to prior studies, MRI is the most
frequently used technology to identify brain tumors (BT) utilizing DL models.
The CNN model and SSAE structures outperformed each other in this review study
using this imaging technology to identify and classify BT. Another technique
Nuclear imaging provides metabolic as well as functional information, which
sets it apart from other imaging techniques. Among several studies in this
context, the RGB-CNN structure shows better results in diagnosing cardiac
diseases. It offers preliminary information that aids doctors in analyzing
organ function and radioactive material activity. Using this imaging modality,
relatively less work has been conducted. As a result, there are a lot of
opportunities for future study employing this modality.

Despite its valuable contributions, this chapter has some limitations.
Firstly, it lacks a comprehensive analysis of the imaging modalities, as well
as an explanation of how DL structures work. Instead, it mainly focuses on
studies that employed DL models in imaging modalities as part of a diagnostic
approach. Nonetheless, the chapter highlights the crucial role that imaging
modalities play in identifying illnesses, as they can assist in diagnosing
various diseases. To further illustrate this, the study provides a few
specific, widely used diagnoses to observe how the analysis of these
modalities using DL approaches can be beneficial.

## References

  * [1] Medical Imaging. In: Wikipedia.https://en.wikipedia.org/wiki/Medical-imaging. Accessed 29 Jan 2023
  * [2] Narin A, Kaya C, Pamuk Z (2021) Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks. Pattern Analysis and Applications 24:1207–1220. doi: 10.1007/s10044-021-00984-y
  * [3] Shah, Faisal Muhammad and Hossain, Tonmoy and Ashraf, Mohsena and Shishir, Fairuz Shadmani and Al Nasim, MD Abdullah and Kabir, Md Hasanul. "Brain Tumor Segmentation Techniques on Medical Images-A Review".
  * [4] (2023) X-ray. In: Wikipedia. https://en.wikipedia.org/wiki/X-ray. Accessed 29 Jan 2023
  * [5] Nasim, MD and Dhali, Aditi and Afrin, Faria and Zaman, Noshin Tasnim and Karim, Nazmul."The prominence of artificial intelligence in covid-19." arXiv preprint arXiv:2111.09537.2021.
  * [6] Center for Devices and Radiological Health Medical X-ray imaging: FDA. In: U.S. Food and Drug Administration. https://www.fda.gov/radiation-emitting-products/medical-imaging/medical-x-ray-imaging. Accessed 29 Jan 2023
  * [7] X-ray: What it is, types, preparation and risks. In: Cleveland Clinic. https://my.clevelandclinic.org/health/diagnostics/21818-x-ray. Accessed 29 Jan 2023
  * [8] 2021-03-16, Team E The invention of Magnetic Resonance Imaging (MRI). In: IEC. https://www.iec.ch/blog/invention-magnetic-resonance-imaging-mri. Accessed 29 Jan 2023
  * [9] (2021) MRI. In: Mayo Clinic. https://www.mayoclinic.org/tests-procedures/mri/about/pac-20384768. Accessed 29 Jan 2023
  * [10] (2023) Nuclear medicine. In: Wikipedia. https://en.wikipedia.org/wiki/Nuclear-medicine. Accessed 29 Jan 2023
  * [11] Apostolopoulos ID, Mpesiana TA (2020) COVID-19: Automatic detection from X-ray images utilizing transfer learning with Convolutional Neural Networks. Physical and Engineering Sciences in Medicine 43:635–640. doi: 10.1007/s13246-020-00865-4
  * [12] Ozturk T, Talo M, Yildirim EA, et al (2020) Automated detection of COVID-19 cases using deep neural networks with X-ray images. Computers in Biology and Medicine 121:103792. doi: 10.1016/j.compbiomed.2020.103792
  * [13] Hemdan EE-D, Shouman MA, Karar ME (2020) COVIDX-Net: A framework of deep learning classifiers to diagnose COVID-19 in X-ray images. In: arXiv.org. https://doi.org/10.48550/arXiv.2003.11055. Accessed 29 Jan 2023
  * [14] Wang L, Lin ZQ, Wong A (2020) Covid-net: A tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images. Scientific Reports. doi: 10.1038/s41598-020-76550-z
  * [15] Kanade PB, Gumaste PP (2015) Brain tumor detection using MRI images. IJIREEICE 146–150. doi: 10.17148/ijireeice.2015.3231
  * [16] Saladi S, Karuna Y, Koppu S, et al (2023) Segmentation and analysis emphasizing neonatal MRI brain images using machine learning techniques. Mathematics 11:285. doi: 10.3390/math11020285
  * [17] Biswas A, Islam MS (2021) Brain tumor types classification using K-means clustering and Ann Approach. 2021 2nd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST). doi: 10.1109/icrest51555.2021.9331115
  * [18] Bhanothu Y, Kamalakannan A, Rajamanickam G (2020) Detection and classification of brain tumor in MRI images using deep convolutional network. 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS). doi: 10.1109/icaccs48705.2020.9074375
  * [19] Toğaçar M, Cömert Z, Ergen B (2020) Classification of brain MRI using hyper column technique with Convolutional Neural Network and feature selection method. Expert Systems with Applications 149:113274. doi: 10.1016/j.eswa.2020.113274
  * [20] Amin J, Sharif M, Gul N, et al (2020) Brain tumor classification based on DWT fusion of MRI sequences using convolutional neural network. Pattern Recognition Letters 129:115–122. doi: 10.1016/j.patrec.2019.11.016
  * [21] Munley MT, Marks LB, Scarfone C, et al (1999) Multimodality Nuclear Medicine Imaging in three-dimensional radiation treatment planning for lung cancer: Challenges and prospects. Lung Cancer 23:105–114. doi: 10.1016/s0169-5002(99)00005-7
  * [22] Marta S, Hiromoto K, Alves Togni PH, dos Santos MJ (2013) Clinical applications of Nuclear Medicine. Medical Imaging in Clinical Practice. doi: 10.5772/53029
  * [23] Prieto E, Martí-Climent JM, Domínguez-Prado I, et al (2011) Voxel-based analysis of dual-time-point 18F-FDG pet images for brain tumor identification and delineation. Journal of Nuclear Medicine 52:865–872. doi: 10.2967/jnumed.110.085324
  * [24] Shinoura N, Nishijima M, Hara T, et al (1997) Brain tumors: Detection with C-11 choline pet. Radiology 202:497–503. doi: 10.1148/radiology.202.2.9015080
  * [25] Wong TZ, van der Westhuizen GJ, Coleman RE (2002) Positron emission tomography imaging of brain tumors. Neuroimaging Clinics of North America 12:615–626. doi: 10.1016/s1052-5149(02)00033-3
  * [26] Koon-Pong Wong, Dagan Feng, Meikle SR, Fulham MJ (2002) Segmentation of dynamic pet images using cluster analysis. IEEE Transactions on Nuclear Science 49:200–207. doi: 10.1109/tns.2002.998752
  * [27] Park Y-J, Choi D, Choi JY, Hyun SH (2021) Performance evaluation of a deep learning system for differential diagnosis of lung cancer with conventional CT and FDG PET/CT using transfer learning and Metadata. Clinical Nuclear Medicine 46:635–640. doi: 10.1097/rlu.0000000000003661
  * [28] Papandrianos N, Papageorgiou E (2021) Automatic diagnosis of coronary artery disease in SPECT myocardial perfusion imaging employing deep learning. Applied Sciences 11:6362. doi: 10.3390/app11146362
  * [29] Papandrianos NI, Feleki A, Papageorgiou EI, Martini C (2022) Deep learning-based automated diagnosis for coronary artery disease using SPECT-MPI Images. Journal of Clinical Medicine 11:3918. doi: 10.3390/jcm11133918
  * [30] ieee8023 IEEE8023/covid-chestxray-dataset: We are building an open database of COVID-19 cases with chest X-ray or CT images. In: GitHub. https://github.com/ieee8023/covid-chestxray-dataset. Accessed 30 Jan 2023
  * [31] Wang X, Peng Y, Lu L, et al (2017) Chestx-Ray8: Hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). doi: 10.1109/cvpr.2017.369
  * [32] Toğaçar M, Ergen B, Cömert Z (2020) Covid-19 detection using deep learning models to exploit social mimic optimization and structured chest X-ray images using fuzzy color and stacking approaches. Computers in Biology and Medicine 121:103805. doi: 10.1016/j.compbiomed.2020.103805
  * [33] Badža MM, Barjaktarović MČ (2020) Classification of brain tumors from MRI images using a convolutional neural network. Applied Sciences 10:1999. doi: 10.3390/app10061999
  * [34] Sahoo L, Sarangi L, Dash BR, Palo HK (2020) Detection and classification of brain tumor using Magnetic Resonance Images. Advances in Electrical Control and Signal Systems 429–441. doi: 10.1007/978-981-15-5262-5_31
  * [35] Kalaiselvi T, Padmapriya ST, Sriramakrishnan P, Somasundaram K (2020) Deriving tumor detection models using convolutional neural networks from MRI of human brain scans. International Journal of Information Technology 12:403–408. doi: 10.1007/s41870-020-00438-4
  * [36] Menze BH, Jakab A, Bauer S, et al (2015) The Multimodal Brain Tumor Image Segmentation Benchmark (brats). IEEE Transactions on Medical Imaging 34:1993–2024. doi: 10.1109/tmi.2014.2377694
  * [37] Papers with code - brats 2018 dataset. In: Dataset | Papers With Code. https://paperswithcode.com/dataset/brats-2018-1. Accessed 30 Jan 2023
  * [38] Oasis Brains. In: OASIS Brains - Open Access Series of Imaging Studies. https://www.oasis-brains.org/. Accessed 30 Jan 2023
  * [39] NITRC: IBSR: Tool/Resource Info. In: N I T R C. https://www.nitrc.org/projects/ibsr. Accessed 30 Jan 2023
  * [40] Find open datasets and Machine Learning Projects. In: Kaggle. https://www.kaggle.com/datasets. Accessed 30 Jan 2023
  * [41] Overview. In: brain. https://portal.brain-map.org/explor/overview?gclid=EAIaIQo
bChMIvIrRtoDJ_AIVwppmAh17RwxyEAAYASAAEgLUNvD_BwE. Accessed 30 Jan 2023

  * [42] B A (2020) Tumor classification using block wise fine tuning and transfer learning of deep neural network and KNN classifier on mr brain images. International Journal of Emerging Trends in Engineering Research 8:574–583. doi: 10.30534/ijeter/2020/48822020
  * [43] Tonmoy, Hossain and Shadmani, Shishir Fairuz and Mohsena, Ashraf and Abdullah, MD Al Nasim and Faisal, Muhammad Shah. "Brain Tumor Detection Using Convolutional Neural Network". 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), IEEE,1–62019.
  * [44] Gopal NN, Karnan M (2010) Diagnose brain tumor through MRI using image processing clustering algorithms such as fuzzy C means along with intelligent optimization techniques. 2010 IEEE International Conference on Computational Intelligence and Computing Research. doi: 10.1109/iccic.2010.5705890
  * [45] (2019) Nuclear medicine. In: Nuclear Medicine | Johns Hopkins Medicine. https://www.hopkinsmedicine.org/health/treatment-tests-and-therapies/nuclear-medicine. Accessed 30 Jan 2023
  * [46] Nuclear imaging. In: Stanford Health Care (SHC) - Stanford Medical Center. https://stanfordhealthcare.org/medical-tests/n/nuclear-imaging.html. Accessed 30 Jan 2023
  * [47] Minen G (2022) Spect vs pet: Radiology reference article. In: Radiopaedia Blog RSS. https://radiopaedia.org/articles/spect-vs-pet. Accessed 30 Jan 2023
  * [48] El-Feky M (2022) Single Photon Emission Computed Tomography (SPECT): Radiology reference article. In: Radiopaedia Blog RSS. https://radiopaedia.org/articles/single-photon-emission-computed-tomography-spect. Accessed 30 Jan 2023
  * [49] Bickle I (2022) Positron Emission Tomography: Radiology Reference Article. In: Radiopaedia Blog RSS. https://radiopaedia.org/articles/positron-emission-tomography?lang=us. Accessed 30 Jan 2023
  * [50] Nayak SR, Nayak DR, Sinha U, et al (2021) Application of deep learning techniques for detection of COVID-19 cases using chest X-ray images: A comprehensive study. Biomedical Signal Processing and Control 64:102365. doi: 10.1016/j.bspc.2020.102365
  * [51] Ucar F, Korkmaz D (2020) Covidiagnosis-net: Deep Bayes-Squeezenet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images. Medical Hypotheses 140:109761. doi: 10.1016/j.mehy.2020.109761
  * [52] Rahman T (2022) Covid-19 radiography database. In: Kaggle. https://www.kaggle.com/datasets/tawsifurrahman/covid19-radiography-database. Accessed 30 Jan 2023
  * [53] Sethy PK, Behera SK (2020) Detection of coronavirus disease (covid-19) based on Deep features. doi: 10.20944/preprints202003.0300.v1
  * [54] Farooq M, Hafeez A (1970) [PDF] covid-resnet: A deep learning framework for screening of covid19 from radiographs: Semantic scholar. In: ArXiv. https://www.semanticscholar.org/paper/COVID-ResNet%3A-A-Deep-Learning-Framework-for-of-from-Farooq-Hafeez/049ea432acc08b04a3e21c390f62be3d845a2e2c. Accessed 30 Jan 2023
  * [55] Al Nasim, Md Abdullah and Al Munem, Abdullah and Islam, Maksuda and Palash, Md Aminul Haque and Haque, Md Mahim Anjum and Shah, Faisal Muhammad. "Brain Tumor Segmentation using Enhanced U-Net Model with Empirical Analysis.", 2022 25th International Conference on Computer and Information Technology (ICCIT),1027–1032, 2022,IEEE.
  * [56] Amin J, Sharif M, Gul N, et al (2019) Brain tumor detection by using stacked autoencoders in deep learning. Journal of Medical Systems. doi: 10.1007/s10916-019-1483-2
  * [57] Ghassemi N, Shoeibi A, Rouhani M (2020) Deep neural network with generative adversarial networks pre-training for Brain Tumor Classification based on Mr Images. Biomedical Signal Processing and Control 57:101678. doi: 10.1016/j.bspc.2019.101678
  * [58] Cheng J, Huang W, Cao S, et al (2015) Enhanced performance of Brain Tumor Classification via tumor region augmentation and partition. PLOS ONE. doi: 10.1371/journal.pone.0140381
  * [59] Marcus DS, Fotenos AF, Csernansky JG, et al (2010) Open access series of imaging studies: Longitudinal MRI data in nondemented and demented older adults. Journal of Cognitive Neuroscience 22:2677–2684. doi: 10.1162/jocn.2009.21407
  * [60] Begum SS, Lakshmi DR (2020) Combining optimal wavelet statistical texture and recurrent neural network for tumour detection and classification over MRI. Multimedia Tools and Applications 79:14009–14030. doi: 10.1007/s11042-020-08643-w
  * [61] Arsanjani R, Dey D, Khachatryan T, et al (2014) Prediction of revascularization after myocardial perfusion SPECT by machine learning in a large population. Journal of Nuclear Cardiology 22:877–884. doi: 10.1007/s12350-014-0027-x
  * [62] Kaplan Berkaya S, Ak Sivrikoz I, Gunal S (2020) Classification models for SPECT myocardial perfusion imaging. Computers in Biology and Medicine 123:103893. doi: 10.1016/j.compbiomed.2020.103893.
  * [63] Puppala, Sai, Ismail Hossain, and Sajedul Talukder. "Towards Federated Learning Based Contraband Detection Within Airport Baggage X-Rays." In 2022 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT), pp. 1-6. IEEE, 2022.
  * [64] Talukder, Sajedul, Sai Puppala, and Ismail Hossain. "Federated Learning-Based Contraband Detection within Airport Baggage X-Rays." Journal of Computing Sciences in Colleges 38, no. 3 (2022): 218-218.
  * [65] Talukder, Sajedul, Sai Puppala, and Ismail Hossain. "A Novel Hierarchical Federated Learning with Self-Regulated Decentralized Clustering." Journal of Computing Sciences in Colleges 38, no. 3 (2022): 222-223.
  * [66] Hossain, Ismail, Sai Puppala, and Sajedul Talukder. "Collaborative Differentially Private Federated Learning Framework for the Prediction of Diabetic Retinopathy." In 2023 IEEE 2nd International Conference on AI in Cybersecurity (ICAIC), pp. 1-6. IEEE, 2023.
  * [67] Hossain, Tonmoy and Shishir, Fairuz Shadmani and Ashraf, Mohsena and Al Nasim4&, MD Abdullah and Shah, Faisal Muhammad, "Brain Tumor Detection Using Convolutional Neural Network".
  * [68] Nasim, MD and Dhali, Aditi and Afrin, Faria and Zaman, Noshin Tasnim and Karim, Nazmul. "The prominence of artificial intelligence in covid-19." 2021