#  Machine Learning-based Lung and Colon Cancer Detection using Deep Feature
Extraction and Ensemble Learning

Md. Alamin Talukder<EMAIL_ADDRESS>Md. Manowarul Islam
<EMAIL_ADDRESS>Md Ashraf Uddin<EMAIL_ADDRESS>Arnisha Akhter
<EMAIL_ADDRESS>Department of Computer Science and Engineering,
Jagannath University, Dhaka, Bangladesh  Khondokar Fida Hasan
<EMAIL_ADDRESS>Centre for Cyber Security Research & Innovation, RMIT
University, 124 La Trobe Street, Melbourne, 3000, VIC, Australia  Mohammad Ali
Moni<EMAIL_ADDRESS>Artificial Intelligence & Data Science, School of
Health and Rehabilitation Sciences, Faculty of Health and Behavioural
Sciences, The University of Queensland St Lucia, QLD 4072, Australia.

###### Abstract

Cancer is a fatal disease caused by a combination of genetic diseases and a
variety of biochemical abnormalities. Lung and colon cancer have emerged as
two of the leading causes of death and disability in humans. The
histopathological detection of such malignancies is usually the most important
component in determining the best course of action. Early detection of the
ailment on either front considerably decreases the likelihood of mortality.
Machine learning and deep learning techniques can be utilized to speed up such
cancer detection, allowing researchers to study a large number of patients in
a much shorter amount of time and at a lower cost. In this research work, we
introduced a hybrid ensemble feature extraction model to efficiently identify
lung and colon cancer. It integrates deep feature extraction and ensemble
learning with high-performance filtering for cancer image datasets. The model
is evaluated on histopathological (LC25000) lung and colon datasets. According
to the study findings, our hybrid model can detect lung, colon, and (lung and
colon) cancer with accuracy rates of 99.05%, 100%, and 99.30%, respectively.
The study’s findings show that our proposed strategy outperforms existing
models significantly. Thus, these models could be applicable in clinics to
support the doctor in the diagnosis of cancers.

###### keywords:

Feature Extraction , Transfer Learning , Machine Learning , Ensemble Learning
, Lung , Colon Cancer

††journal: Journal of LaTeX Templates

##  1 Introduction

Cancer is a broad classification that encompasses a variety of illnesses that
can damage organs in the human body. It is also a rapid reaction of aberrant
cells that expand beyond their acceptable boundaries, allowing them to infect
neighboring areas and travel to neighboring tissues which are known as
metastasis. Metastasis is the prime factors of cancer-related death [55]. The
disease’s prevalence has risen significantly over time, most likely as a
result of increased sensitivity to particular cancers as age advances.
Colorectal cancer, which affects the colon and rectum, is one of the major
induces of death. Lung cancer is probably the most commonly recognized
malignancy (12.2%), but colorectal cancer, which affects the colon and rectum,
is also the leading trigger of death (10.7%) [48]. It is one of the major
drivers of fatality globally, although its impact is not evenly distributed
[80]. When it comes to identifying cancer symptoms, there are quite a variety
of approaches. With the advancements, the amount of data stored in archives is
growing by the day [28]. It is extremely challenging to analyze a huge
quantity of data and extrapolate using traditional approaches [87]. The
increased availability of healthcare data provides scientists with a fresh
opportunity to improve existing approaches for further comprehensive clinical
analysis [68]. In the medical profession, machine learning and deep learning
are commonly utilised technologies for analyzing biomedical data [58] [20] [9]
[45].

Machine learning (ML) is a limb of AI technologies that arose from the field
of pattern identification and cognitive acquisition concepts. It produces
mechanisms that effectively adapt through a vast group of information and
generate forecasts based on historical evidence [58]. In several sectors where
developing explicit techniques with acceptable efficiency seemed hard and
nearly impossible, machine learning had indeed been successfully employed with
remarkable outcomes [37] [8] [83]. Deep learning (DL), on the contrary, is a
sophisticated based pattern classification technology that has shown
exceptional results in feature extraction, object detection and voice
recognition, and other domains that require multilevel data processing [62].
It can extract meaningful significant patterns from images and has been
demonstrated to obtain state efficiency, occasionally outperforming humans
[19]. Transfer learning (TL) entails repurposing previously acquired knowledge
to tackle a specific situation [38]. It can be used in two ways: as a baseline
algorithm, which is used to train the image dataset and determine performance
[34] [18] [41]; as a feature extractor, which is used to extract features from
image datasets and then use machine or deep learning algorithms to determine
performance [23] [22] [1] [60]. Ensemble learning relies upon the combination
of many learning models that are purposefully developed as well as integrated
to solve issues like categorization [61]. It is a kind of machine learning
that aims to upgrade predicted efficiency by combining several models.
Moreover, it’s a significant subject for increasing base classification models
[54].

A number of papers have been conducted in the literature, each of which is
predicated on a distinct strategy for detecting cancer [33] [74] [46] [15]. A
wide variety of medical images have already been identified and signified with
the help of machine learning procedures. DL strategies have made it possible
for machines to assess exalted dimensional information such as pictures,
multimodal pathology scans, and video files [3] [7] [77]. Numerous supervised
machine learning techniques have been produced, as they are extremely adept at
handling biological images [13] [82] [57] [78].

To classify lung and colon cancer efficiently, we need to train our model with
a large dataset where all cancer possibilities are included. To do so, our
research emphasizes designing a resourceful and well-organized framework where
we can take large histopathological image (HPI) datasets (LC25000) and a
number of pre-processing methods are applied to datasets. k-fold cross-
validation, and feature extraction, all are applied to it. To build a robust
model, we have used an ensemble method at the end of our implementations
getting better performance in cancer classifications.

The performance of our proposed hybrid model will be evaluated using high-
performance filtering (HPF) with combination of ensemble learning. We have
evaluated a variety of performance indicators in our investigation including
accuracy, recall, precision , f1-score, MAE, MSE, RMSE, confusion matrix, AUC
score and the ROC Curve. The classifier algorithms in the proposed framework
can detect cancer (lung and colon) having an accuracy rate of above 99%.

The following are the major accomplishments of this study.

  * 1.

First of all, to detect lung and colon cancer, we proposed an efficient hybrid
technique that combines deep feature extraction, high-performance filtering,
and an ensemble learning strategy.

  * 2.

Secondly, A deep feature extraction technique is proposed that is uniquely
applied using different transfer learning models to extract features.

  * 3.

Finally, we analyze our proposed model on HPI datasets using a set of metrics
and showed how to diagnose lung and colon cancers, thereby contributing to the
new knowledge of AI-based applications.

The remainder of the paper is arranged as follows: Section 2 leads a
literature review to identify the state-of-art of lung and colon cancer
diagnoses using machine learning. Section 3 gives the methodology and the
dataset description of the study. The experimental setup and performance
evaluation are presented in Section 4 and Section 5, respectively before
concluding the paper in section 6.

##  2 Literature Review

Categorizing histopathological image datasets of many cancer kinds, such as
breast, lung, colon, colorectal, and skin cancer, has received much attention.
Various approaches are utilized using ML, DL, and TL to detect lung and colon
cancer.

###  2.1 Lung and colon cancer using computer-aided diagnosis (CAD) approach

A CAD method was introduced by [53] to categorize histopathology images of
lung tissues automatically. They evaluated the effectiveness of eight ML
algorithms on two datasets using traditional texture analysis (TA) and
homology-based image processing (HI) to visual feature extraction. The CAD
system with HI outperformed the TA system in both datasets. As a result, they
determined that HI was much more beneficial for CAD systems than TA and that a
precise CAD system for lung tissues might be developed. Similarly, [49]
created a CAD system by examining digital pathology images for recognizing
lung and colon cancer utilizing a convolutional neural network (CNN). In
comparison to classical ML models as well as deep CNN models utilizing TL
trained on a similar collection though utilizing state-of-the-art feature
descriptors, their empirical findings on the LC25000 indicated better accuracy
of 97.89% for lung and 96.61% for colon gained by CNN. Moreover, [69] devised
a CAD technique for categorizing histological pictures of lung tissues. They
utilized a publicly accessible dataset of histological photos of lung tissue
for the design and validation of CAD. Multi-scale processing was performed to
extract image features. Furthermore, a comparative analysis was conducted
using seven pre-trained hyper-tuned CNN models for lung cancer prediction
where, ResNet101 provided the highest accuracy of all, at 98.67 percent. This
study will assist researchers in developing more effective CNN-based lung
cancer detection models.

###  2.2 Lung and colon cancer using CNN approach

A CNN approach was proposed by [33] for detecting lung cancer using
histopathology pictures. Their model revealed a considerable improvement in
classification accuracy and got training and validation accuracy of 96.11% and
97.20%, respectively. Similarly, [74] introduced three CNN models to analyze
colon cell imaging data. The models were trained and tested at various epochs
to estimate the learning rate. The average pooling and max-pooling layers are
revealed to be 95.48% and 97.49% accurate, respectively. The MobileNetV2
exceeds the other two models with the highest accuracy of 99.67% and the
lowest loss rate of 1.24%. It would be developed in collaboration with medical
professionals at hospitals or clinics that specialize in colon cancer
treatment. [63] produced a CNN model for predicting colon cancer distinguished
by speed and accuracy with a small number of parameters. Their model was based
on two routes, each of which was in charge of building 256 feature maps to
enhance the number of features at various levels and so raise the accuracy and
sensitivity. The visual geometry group (VGG16) model was created and trained
within the same dataset to assess the effectiveness of the proposed scheme.
The acquired accuracy is 99.6% for the proposed model and 96.2% for VGG16. The
performance indicated that it was useful in identifying colon cancer. On the
other hand, [72] proposed a unique method for segmenting, recognizing,
classifying, and detecting various types of malignant cells in both MRI and
RGB scans. They used a CNN model and a SegNet approach with a morphological
operation that was superior to the typical SegNet model to minimize training
time and boost segmentation results. The proposed technique produced an
average accuracy rate of 93 percent in detecting cancer cells from multiple
cancer datasets. They were able to overcome the limitations of employing
separate cancer identification techniques for MRI and Histopathology data.

###  2.3 Lung and colon cancer using DL and ML approach

An ML approach to diagnosing lung and colon cancer based on DL was proposed by
[50] that analyzed pathological photos of lung and colon cancers to identify
five different types of tissues. They extracted the features using 2D Fourier
and 2D Wavelet (2D FW) feature extraction process and combine the features and
trained them with a CNN model. The obtained findings revealed that the
presented architecture has the highest accuracy of 96.33 cents in identifying
cancer tissues. The use of this approach will aid healthcare practitioners in
the development of a computerized and efficient method for detecting different
forms of lung and colon cancers. Similarly, [68] evaluated the effectiveness
of deep feature extraction for colorectal cancer diagnosis using two TL models
as well as traditional ML algorithms. The findings revealed that employing
DenseNet121 to extract the features outperforms ResNet50 for all ML algorithms
and achieved 98.53% accuracy utilizing the KNN algorithm. The developed
approach proved adequate for identifying colon tissue. To enhance picture
classification efficiency, [10] presented an approach integrating deep
features generated with VGG19 with other customized feature extraction
techniques, such as SIFT, SURF, ORB, and the Shi-Tomasi corner detector
algorithm. Additionally, the collected features from these methods were
categorized using a variety of ML algorithms. The empirical findings showed
that Random Forest (RF) with the cooperative features beat other classifiers
with 93.73 percent accuracy. According to the findings, a single feature
extractor is insufficient to provide consistent outcomes. As a result, for
picture classification, a mixed method employing DL features as well as
classical handmade features is preferable. [76] introduced a DL model where
the image classes were built from the ground up using the DarkNet19 model. The
feeble features were selected from the feature set retrieved from the
DarkNet19 model using the Equilibrium and Manta Ray Foraging optimization
approach and separated from the rest of the set’s features, yielding an
optimal feature set. The Support Vector Machine (SVM) approach was accustomed
to aggregating and categorizing the suitable characteristics generated by the
two optimization algorithms utilized. The overall classifier performance
percentage was 99.69%. It was discovered that combining the complementary
approach with certain optimizing strategies increased the dataset’s
categorization ability. To determine lung and colon cancer using
histopathological images with improved augmentation strategies, [27] proposed
a method by modifying 8 TL models. According to the findings, the 8 models
achieved notable accuracy ranging from 96 percent to 100 percent accuracy. To
visualize the focus images of CNN models, SmoothGrad and GradCAM were then
employed that had previously been learned to differentiate between cancerous
and benign images.

###  2.4 Lung and colon cancer using Hybrid approach

In [60], for diagnosing lung cancer three TL models were utilized. The
findings exhibited that the proposed models attained accuracy levels of 62
percent, 90 percent, and 89 percent for VGG16, ResNet50V2, and DenseNet201,
respectively. Lastly, a combination of the three proposed CNN models called
the ensemble model exceeded the other models with a validation accuracy of
91%. The employment of the ensembles of the TL model, which enhances
efficiency, is beneficial in overcoming the challenge of diagnosing lung
cancer. Another, [15] proposed a hybrid classifying framework that combines
the Inception v3, hog, and daisy extracting features modules to distinguish
lung cancer and normal tissue from lung pathology images. The experimental
findings showed that the proposed model achieved an accuracy of 99.60 percent,
which was a gratifying outcome greater than other comparable trials. It
demonstrated that hybrid deep learning models may be used to evaluate a
reliable cancer diagnosis approach. On the other hand, [46] developed a
shearlet transform-based multi-scale feature fusion (MFF-CNN) model to
recognize histopathology images of colon cancer. It achieved a 96 percent
detection performance for colorectal after feature learning and feature
fusion. Its improvement offers innovative opportunities for cancer screening
instantaneously, with objectivity as well as accuracy. [87] created a new
method for identifying colon cancer images called MA ColonNET. The algorithm
reached a 99.75 cent accuracy rate for detecting and classifying colon cancer
which will help to avoid human errors that are common in older methods. They
demonstrated that the presented methodology can identify colon cancer faster
than originally thought. The therapy strategy will be more effective as a
result of this as well. Furthermore, [4] offered the DHS-CapsNet, a
revolutionary dual horizontal squash capsule network for classifying lung and
colon tumors on histopathological pictures. Encoder feature fusion (EFF) and a
unique horizontal squash (HSquash) algorithm were used to build DHS-CapsNet.
The collected feature from the 2-lane convolutional layers was aggregated by
the EFF, which provided valuable insights for greater accuracy. The claiming
prediction error for the DHS-CapsNet was 0.77 percent, as opposed to 14.45
percent for the regular CapsNet. It enhanced CapsNet which can be used as a
CAD approach to aid clinicians in diagnosing lung and colon cancer. The
related paper summary is shown in Table 1.

SL. No | Author | Dataset | Approach | Accuracy(In %)
---|---|---|---|---
1 | [53] | Histopathology | CAD | 99.23
2 | [49] | Histopathology | CAD |  | 97 (Lung)
---
96 (Colon)
3 | [69] | Histopathology | CAD | 98.67
4 | [33] | Histopathology | CNN | 97.2
5 | [74] | Histopathology | CNN | 99.67
6 | [63] | Histopathology | CNN | 99.6
7 | [72] |  | Brain MRI Image
---
BreCaHAD
SN-AM
Histopathology
CNN |  | 98.59 (IoU)
---
91.16 (IoU)
93.02 (IoU)
90.24 (IoU)
8 | [50] | Histopathology | 2D FW+ CNN | 96.33
9 | [68] | Histopathology | DenseNet121 + KNN | 98.53
10 | [10] | Caltech101 | VGG19 + RF | 93.73
11 | [76] | Histopathology | DarkNet19 + SVM | 99.69
12 | [27] | Histopathology |  | VGG16
---
ResNet50
InceptionV3
InceptionResNetV2
MobileNet
Xception
NASNetMobile
DenseNet169
| 98
---
96
100
100
100
100
97
100
13 | [60] | Histopathology | Ensemble Model | 91
14 | [15] | Histopathology | Ensemble Model | 99.6
15 | [46] | Histopathology | MFF-CNN | 96
16 | [87] | Histopathology | MA ColonNET | 99.75
17 | [4] | Histopathology | DHS-CapsNet | 99.23
Table 1: Related work summary

##  3 Methodology

In this section, we have covered image pre-processing, deep feature
extraction, high-performance filtering, and ensemble learning method as well
as our proposed model. We also give a basic rundown of the ML techniques that
are used to detect lung and colon cancer.

We developed our proposed model using feature extraction and machine learning
algorithms on LC25000 lung and colon histopathological image datasets to
assure a prognosis of lung and colon cancer. Figure 1 represents the schematic
block diagram of our proposed paradigm. The stages of our proposed model are
as follows:

  * 1.

Step-1: Initially, we take the histopathological image datasets (LC25000) for
lung and colon and combine both (lung and colon) cancer to conduct our
experiment. There are three types of lung cancers, and two types of colon
cancers are available in our datasets.

  * 2.

Step-2: In the pre-processing step, the pre-processing is accomplished by
resizing the image into 128x128, converting the image into bgr2rgb, and then
the image to NumPy array. After that, we perform feature scaling where we use
the generalization method in the image; we divide easy image array values by
255, which is the maximum value (max. intensity value of an image), and
perform labeling by assigning a label (0,1,2) for each image.

  * 3.

Step-3: In the deep feature extraction step, we extract features from the
image datasets using five different transfer learning such as VGG16, VGG19,
DenseNet169, DenseNet201 and collect the features for next the step.

  * 4.

Step-4: In this step, successfully extracted features are fit into the six
well-known ML algorithms such as Random Forest (RF), Support Vector Machine
(SVM), Logistic Regression (LR), Multi-Layer Perceptron (MLP), Extreme
Gradient Boosting (XGB), and Light Gradient Boosting (LGB) to evaluate the
performance.

  * 5.

Step-5: In this High-Performance Filtering (HPF) step, we filter our ML
algorithms based on the accuracy. By using this filtering, we select the top
three algorithms for the next step.

  * 6.

Step-6: In this Ensemble Learning step, after getting the top three algorithms
from HPF, we apply majority (hard) and weighted average (soft) voting
classifiers on these algorithms for all TL and select the best ensemble voting
classifier for each TL model.

  * 7.

Step-7: Finally, the performance is assessed based on the accuracy for each TL
and selecting the best TL model. After that, the performance metrics such as
accuracy, recall, precision, f1-score, MAE, MSE, RMSE, confusion matrix, AUC
score, and ROC Curve are used to evaluate the selected model of our experiment
and also showed the comparison analysis with other existing models.

Figure 1: The proposed hybrid model for lung and colon cancer detection

###  3.1 Dataset Collection

In our proposed work, we have employed the lung and colon cancer dataset
called histopathological images (LC25000) dataset. The dataset consists of two
cancerous datasets called lung and colon cancer datasets. It contains 25000
images where 10000 for colon cancer and 15000 for lung cancer images. The lung
cancer dataset contains 3 labels of cells such as adenocarcinomas, squamous
cell carcinoma and benign tissue. The colon cancer dataset contains 2 labels
of cells, such as adenocarcinomas and benign tissue. The LC25000 dataset was
created using a snippet of HIPAA-compliant as well as confirmed references,
including 750 lung tissue where 250 adenocarcinomas, 250 squamous cell
carcinomas and 250 benign tissue as well as 500 colon tissue where 250
adenocarcinomas and 250 benign tissue which were then augmented to create
25,000 images [11].

In our experiments, we have used lung, colon, and both (lung and colon) cancer
images. So, we took 10000 images consisting of 5000 images for colon tissue
and 5000 images for lung tissue for both lung and colon; 2800 images for colon
tissue for colon; 4200 images for lung tissue for lung. The distribution of
Cancerous datasets images is shown in Table2, Table 3, and Table4, for our
research.

Lung Cancer | No. of Images
---|---
Benign lung tissue | 1400
Lung adenocarcinomas | 1400
Lung squamous cell carcinoma | 1400
Total | 4200
Table 2: Distribution of Lung Cancer dataset Colon Cancer | No. of Images
---|---
Benign colon tissue | 1400
Colon adenocarcinomas | 1400
Total | 2800
Table 3: Distribution of Colon Cancer dataset Cancer | No. of Images
---|---
Benign lung tissue | 2000
Lung adenocarcinomas | 2000
Lung squamous cell carcinoma | 2000
Benign colon tissue | 2000
Colon adenocarcinomas | 2000
Total | 10000
Table 4: Distribution of Cancer (lung and colon) dataset

###  3.2 Pre-processing

Initially, in our methodology, we downsized the input image to 128x128 while
keeping the same image size to train the model. We transformed the scaled
image into bgr2rgb and then into a NumPy array to work with picture intensity
values. Then, perform scaling, a procedure that normalizes the value of image
intensity values within a range (0 to 1). To reduce computational complexity,
the image is scaled by dividing the image array by 255. Finally, we added the
image label as Image labeling, which is an important step because it allows us
to identify malignant photos. Fig. 2 depicts the pre-processing steps of our
proposed model. Table 5 and Table 6 show labeling for lung and colon cancer
dataset, respectively.

Figure 2: Pre-processing steps Lung Cancer | Dataset | Label
---|---|---
Lung adenocarcinoma | lung_aca | 0
Lung benign tissue | lung_n | 1
Lung squamous cell carcinoma | lung_scc | 2
Table 5: Labeling for lung cancer dataset Colon Cancer | Dataset | Label
---|---|---
Colon adenocarcinoma | colon_aca | 0
Colon benign tissue | colon_n | 1
Table 6: Labeling for colon cancer dataset

After pre-processing, the resultant images are sharper, bright-colored, and
more discernible details are visible than the original which are suitable to
drive into the model and get better performance than others. Fig 3 depicts the
before and after image pre-processing for lung cancer adenocarcinoma, squamous
cell carcinoma, and benign tissue respectively, where the upper part (a,b,c)
are before pre-processing and the lower part (d,e,f) are the after pre-
processing of lung cancer image.

(a) before (b) before (c) before (d) after (e) after (f) after

Figure 3: Lung adenocarcinoma, squamous cell carcinoma, benign tissue before
and after pre-processing

Fig 4 depicts the before and after image pre-processing for colon cancer
adenocarcinoma and colon benign tissue respectively, where a and b are the
before and after pre-processing images for colon adenocarcinoma images and c
and d are the before and after pre-processing image for colon benign tissue
image.

(a) before (b) after (c) before (d) after

Figure 4: Colon adenocarcinoma and benign image before and after pre-
processing

###  3.3 Deep Feature Extraction

Transfer learning is the method of applying features learned on one problem to
solve a new, related problem [26]. This deep learning approach has received a
lot of attention, so we adopt several existing models in our methodology and
adjust it to fit our needs. Rather than training all of the model’s layers, we
isolate a few and use the trained weights in those frozen layers to derive
certain properties from our datasets [75]. The significant properties of an
image have a big impact on how well an identification operation works. In our
paper, we analyze our datasets using five different TL models such as VGG16,
VGG19, MobileNet, DenseNet169, and DenseNet201 to extract features in addition
to assessing each individual’s effectiveness by using various ML algorithms.

  * 1.

VGG16, a CNN model produced by the Visual Geometry Group (VGG) of the
University of Oxford, was named the winner of the 2014 ILSVRC object
identification algorithm [39]. VGG16’s main contribution is to show that
increasing network depth can enhance network performance in some cases. VGG16
improves on the standard AlexNet by using numerous 33 convolution cores to
substitute the larger convolution cores (1111, 707, 55), which can extend the
depth of the network and minimize the number of network parameters [84]. There
are 3 parts to VGG16 transfer learning such as convolution, pooling, and fully
connected layers. Filters are employed in the convolution layer to extract
information from images, and the kernel as well as stride size are the most
significant characteristics in this layer. To lessen the dimensionality and
calculations, the poling layer is employed to shrink the network’s spatial
size. They have fully connected interactions with the previous layers in fully
connected layers.

  * 2.

The VGG19 transfer learning model, which is also a CNN model, was introduced
by [73]. It is a 19 layers weighted ground feature extractor that is
extensively utilized [47]. The consisting of it’s layers is the combination of
16 convnet and 3 dense layers which are the linked layers to categorize
pictures into 1000 item categories. The ImageNet repository was employed to
develop VGG19, which comprises over a million pictures separated into 1000
classes. The sequence of the model is 2 conv 1 max.pool, 2 conv 1 max.pool, 4
conv 1 max.pool, 4 conv 1 max.pool, 4 conv 1 max.pool and 3 FC layers. Due to
the employment of numerous 3x3 filters within every convnet, it is a widely
common picture predictive model.

  * 3.

One of the Convolutional networks that have already been trained is MobileNet
[35]. The quantity of parameters that have been lower than others
distinguishes MobileNet from other existing models. It is built on depthwise
separable convolutions, which were developed in [71] to reduce the computing
effort in the initial few layers, depthwise and pointwise convolutions are two
layers by which it is constituted. The technique of filtering the input
without creating additional features is known as depthwise convolution. As a
result, the pointwise convolution procedure, which generates innovative
features, was integrated. Thus, depthwise separable convolution was coined to
describe the merging of two layers. It assigns a unique filter for each stream
of input using depthwise convolutions and afterwards utilizes 1x1 conv
(pointwise) for constructing a joint distribution of output from the depthwise
layer. Batch Normalization (BN) with Rectified Linear Unit (ReLU) are employed
after each convolution. It’s efficiency was determined by a trade-off between
delay and correctness. Due to the compactness, we can perform our tasks using
this method on the CPU in lieu of GPU. It can be used for categorization,
identification, and segmentation in the same way as previous large-scale
models that have been released [66].

  * 4.

DenseNet is a Dense Convolution Neural Network, introduced by [36]. In
DenseNet, each layer influences all other layers in a feed-forward manner
[30]. There are five levels in a DenseNet block. The very first four layers
are dense, and the final layer is a transition layer. This dense block has a
growth rate of (k) for each layer. If the growth rate is 4 then a 2x2 average
pool with a 1x1 conv and a stride of 2 exists in the transition layer. There
are 1x1 and 3x3 conv with a stride of 1 in the dense layer. Two popular
DenseNet networks such as DenseNet169 and DenseNet201 are utilized in our
research experiment.

Fig. 5 illustrates a sample of the feature extraction procedure where it
includes the original architecture of VGG16 and deep feature extraction from
the VGG16 model. The composition of the original VGG16 CNN model has 13
convolutional (conv) layers, 3 fully connected (FC) layers, and 5 pooling
layers. The sequence of the model is 2 conv 1 pool, 2 conv 1 pool, 3 conv 1
pool, 3 conv 1 pool, 3 conv 1 pool and 3 FC layers. It employs a small kernel
(3x3) with 1 stride on all conv layers. The max pool layer continually derives
after the conv layers. It takes 3 channels of images with a resolution of
224x224. In the 3 FC layers, the first two FC layers have an identical channel
size of 4096 and the last one has a channel size of 1000, which defines the
number of class labels in the ImageNet data. The soft-max layer is the output
layer, and it’s in charge of the input image’s assigned probability. On the
other hand, the deep feature extraction from the VGG16 model is the same as
the original VGG16 model except for the last 3 FC layers. Without involving
the last 3 FC layers, we have just extracted features from this model and
grazed them into different ML models after that we apply HPF and get the top 3
filtered ML algorithms and then apply ensemble learning with these algorithms
and evaluate the performance. The same procedure is applied to all of the TL
models to extract features.

(a) The original VGG16 model (b) Deep feature extracted VGG16 model

Figure 5: A sample architecture of original VGG16 model and deep feature extracted VGG16 model  Dataset | Before feature extraction | After feature extraction
---|---|---
Lung | 4200, 128, 128, 3 | 4200, 16384
Colon | 2800, 128, 128, 3 | 2800, 16384
Cancer(lung and colon) | 10000, 128, 128, 3 | 10000, 16384
Table 7: Shape of feature

In this feature extraction process, we fixed the last 3 FC layers and
extracted the features from the last block of the max-pooling layer. In the
feature extraction process, the before and after feature shape is shown in 7
where the input size of the image is 128*128*3. The final feature size after
feature extraction is 16384. Hence, before feature extraction the size of
features is n*128*128*3, and after extraction n*16384. Here, n is the number
of input images. So, for lung cancer, the input feature size is 4200*128*128*3
and the output feature size is 4200*16384, for colon cancer, the input feature
size is 2800*128*128*3, and the output feature size is 2800*16384, for (lung
and colon) cancer, the input feature size is 10000*128*128*3 and output
feature size is 10000*16384. After that, we provision the extracted features
into the ML algorithms to assess the efficiency of each machine learning
model. This procedure is applied for all of the above described TL models.

###  3.4 Machine Learning Algorithms

In this section, we have described several ML algorithms which is used in our
model to evaluate the performance.

  * 1.

Random Forest (RF) is a meta-approximation that employs averaging to enhance
accuracy level and multiple decision tree classifiers are fitted to various
sub-trials of the dataset to prevent over-fitting [6]. It is a combination of
several uncut DTs derived from different bootstrap samples of the training
data and each attribute subset is sampled separately from the actual feature
space [12]. It creates several independent decision trees from the training
dataset’s original features, then votes to combine all of the trees into a
single classification model [56]. It achieves a better prediction accuracy due
to employing the outcomes of numerous decision trees to construct a prediction
[5].

  * 2.

Support Vector Machine (SVM) is a classification procedure initiated with the
detection of the hyperplane and then the classification of the classes [67].
For classification analysis, SVM is a popular machine learning tool. SVM
increases generalization by reducing the learning machine’s structural risk.
SVM has been used in many contexts because of its high recognition rate and
fast training speed when classifying relatively small datasets [70]. It has
been shown to be an efficient strategy for classification tasks because it
adheres to the proper risk management design to maximize generalizability.
Furthermore, because solving an SVM is effectively a convex optimization
problem, the procured internally optimal solution must also be a globally
optimal one. As a result, SVM is used to train the base classifiers in this
case [31].

  * 3.

Logistic Regression (LR) is a conventional parametric approach that is a
generalized linear model extension that has been typically employed in diverse
areas, including health care [86]. It uses a logit relation equation to
predict the output. Using backward/forward elimination with a p-value less
than 0.05, we can pick the most significant predictor parameters using the
method’s iterative selection approach. The Realimpo package in R 3.3.1
statistical program is used to measure the relative value of each variable
[30]. It conjectures to find out how to generate a categorical output from
input variables and also forecasts a categorical dependent variable’s output.
So, the final output must be either categories or discrete.

  * 4.

Multilayer Perceptron (MLP) is a common ANN architecture that consists of a
series of layers made up of neurons as well as their connections. It can
measure the weighted sum of its inputs before applying an activation function
to generate a signal that will be sent to the next neuron [14]. Between the
input and output layers, it has one or more hidden layers. The neurons are
arranged in layers and connections are often guided from lower to upper
layers, and the neurons in the same layer are not linked[64]. In the input
layer, the number of neurons equals the number of measurements for the pattern
query, while the number of neurons in the output layer equals the number of
classes.

  * 5.

Extreme Gradient Boosting (XGB) is a supervised ML technique that improves
speed and performance by using gradient boosted decision trees. It is fast as
compared to other implementations of gradient boosting [17]. The final
prediction is made by a combination of the residuals of prior models which are
predicted from the generated novel models. It uses a gradient descent strategy
to reduce loss and to boost up the model for improved performance. It’s a
scalable end-to-end tree boosting method that data scientists utilize to
obtain cutting-edge outcomes on a variety of machine learning problems [16].
The objective functions in XGboost consist of two parts training loss and
regularization with $\theta$ the optimal settings for $x_{i}$ training data
and $y_{i}$ labels. Equ. 1 Shows objective functions of XGBboost.

$O(\theta)=L(\theta)+\Omega(\theta)$ (1)

Here, $L$ stands for the training loss function, which indicates how well our
model predicts the training datasets.

  * 6.

Light Gradient Boosting (LGB) is a new gradient learning paradigm based on the
decision tree idea [40]. It outperforms XGBoost, is distinguished by its use
of less storage, its use of a leaf-wise growth technique with depth limits,
and its use of a histogram-based algorithm to speed up the learning process
[85]. LightGBM builds a k-width histogram by discretizing continuum floating-
point eigenvalues into k bins employing the aforementioned histogram
algorithm. Moreover, the histogram approach does not necessitate extra
archiving of pre-sorted findings and results can be saved in an 8-bit integer
upon feature determination, which reduces storage use to 1/8 of what it was
before. In spite of this, the model’s efficiency suffers because of the strict
dividing mechanism [51].

###  3.5 High-Performance Filtering

High-Performance Filtering (HPF) is a process in which we have selected the
top three ML algorithms to feed into our ensemble learning process. The output
of this HPF process is to find the estimators which we can feed into our
ensemble learning model to generate our final output. To optimize the
consistency and accuracy of estimates, we use the best three algorithms in the
ensemble learning process. In some circumstances, if we acquire worse accuracy
in half of the six ML algorithms, the ensemble learning models would not
outperform the base learners. Moreover, if we select the top two ML algorithms
the robustness is comparatively legislation. Hence, to circumvent these
issues, we select the top three or half of the trained ML algorithms.
Moreover, the model’s training time will be comparatively less than the top 4
as it used only top 3 and the accuracy rate will be comparatively higher than
the top 2 as it used top 3.

In the following section, we have tabulated the feature extraction performance
of our experiments as well as visualized the accuracy performance in the
graphical form. In Table 8, 9 and 10 show that among all the algorithms the
SVM, LR, and MLP provide the better performance.

  * 1.

For 8 table, the average accuracy rate is 94.95%, 97.67%, 97.67%, 97.67%,
95.86%, 96.76% for RF, SVM, LR, MLP, XGB, and LGB. Among all SVM, LR, and MLP
give the highest average accuracy than others. Hence, we select these top
three algorithms for lung cancer to fit into the ensemble models and further
analysis.

Algorithm | VGG16 | VGG19 | MobileNet | DenseNet169 | DenseNet201 | Average
---|---|---|---|---|---|---
RF | 93.57 | 94.05 | 95.71 | 94.52 | 96.9 | 94.95
SVM | 96.9 | 97.62 | 98.57 | 97.14 | 98.1 | 97.67
LR | 96.9 | 96.67 | 98.81 | 97.14 | 98.33 | 97.57
MLP | 96.9 | 96.67 | 98.1 | 97.62 | 99.05 | 97.67
XGB | 94.05 | 95.71 | 96.19 | 95.95 | 97.38 | 95.86
LGB | 95.24 | 96.43 | 97.38 | 96.67 | 98.1 | 96.76
Table 8: Accuracy performance of ml algorithms on TL for lung cancer (in %)

  * 2.

For 9 table, the average accuracy rate is 98.29%, 99.57%, 99.57%, 99.57%,
97.79%, 98.64% for RF, SVM, LR, MLP, XGB, and LGB. Among all SVM, LR, and MLP
give the highest average accuracy than others. Hence, we select these top
three algorithms for colon cancer to fit into the ensemble models and further
analysis.

Algorithm | VGG16 | VGG19 | MobileNet | DenseNet169 | DenseNet201 | Average
---|---|---|---|---|---|---
RF | 97.5 | 97.14 | 99.29 | 98.57 | 98.93 | 98.29
SVM | 99.64 | 99.29 | 100.0 | 99.29 | 99.64 | 99.57
LR | 99.64 | 99.29 | 100.0 | 99.29 | 99.64 | 99.57
MLP | 99.64 | 99.29 | 100.0 | 99.29 | 99.64 | 99.57
XGB | 95.36 | 97.5 | 98.93 | 98.21 | 98.93 | 97.79
LGB | 97.14 | 97.86 | 99.64 | 99.29 | 99.29 | 98.64
Table 9: Accuracy performance of ml algorithms on for colon cancer (in %)

  * 3.

For 10 table, the average accuracy rate is 95.8%, 98.54%, 98.42%, 98.58%,
96.70%, 97.28% for RF, SVM, LR, MLP, XGB, and LGB. Among all SVM, LR, and MLP
give the highest average accuracy than others. Hence, we select these top
three algorithms for (lung and colon) cancer to fit into the ensemble models
and for further analysis.

Algorithm | VGG16 | VGG19 | MobileNet | DenseNet169 | DenseNet201 | Average
---|---|---|---|---|---|---
RF | 95.2 | 94.7 | 96.8 | 95.5 | 96.8 | 95.8
SVM | 97.9 | 97.8 | 99.4 | 98.3 | 99.3 | 98.54
LR | 98.0 | 97.6 | 99.2 | 98.1 | 99.2 | 98.42
MLP | 98.4 | 97.9 | 99.0 | 98.3 | 99.3 | 98.58
XGB | 95.6 | 95.6 | 97.0 | 97.5 | 97.8 | 96.7
LGB | 96.1 | 96.2 | 98.5 | 97.7 | 97.9 | 97.28
Table 10: Accuracy performance of ml algorithms on TL for lung and colon
cancer (in %)

(a) lung cancer (b) colon cancer (c) lung and colon cancer

Figure 6: Accuracy performance analysis of ML algorithms on TL for lung and
colon cancer

Fig 6 illustrates that for lung, colon, and lung and colon graphs, among all
the ML algorithms SVM, LR, and MLP outperform for each TL model. As a result,
we select the most performance gained algorithms from all ML algorithms such
as SVM, LR, and MLP for lung, colon and lung and colon cancer.

After successfully filtering the high-performance ML algorithms, we fit these
into our ensemble model to get better performance. For each dataset, we fit
those selected top 3 selected ML algorithms and fit into the two ensemble
voting classifiers namely majority (hard) and weighted average (soft). After
that, we assess the effectiveness of our models for each dataset.

###  3.6 Ensemble Learning

Ensemble learning (EL), also known as multiple classifier or committee-based
learning, is a way of training and integrating numerous learners to
successfully deal with a problem [89]. During the last few generations, it has
sparked a lot of attention, particularly in the AI and machine learning areas.
It is not only justified and fairly efficient, but it is also incredibly
adaptable in a broad variety of essential areas and contributes significantly,
so this attention is pretty well deserved [24]. Regarding majority rule
voting, hard employs anticipated class labeling. Soft estimates each class
label using the argmax where the intended function’s maximum value is
returned, of the summing of the estimated probabilities, which is ideal for
just a group of well-calibrated learners [59].

In soft voting, we forecast the class labels depending on the classifier’s
estimated probabilities [43], where we used averaging the class probability
[65]. In the implementation by default, it calculates the soft voting using a
uniform value. Hence, the weight that is used in the weighted average process
is a uniform value. Moreover, it has been used since the classifiers that we
have used are well-calibrated which means the scores of the classifiers are in
good agreement with probability estimations. The way of the soft voting
procedure is shown in the following equation.

$\hat{y}=\arg max\sum\limits_{j=1}^{n}{({w_{j}}{p_{j}})}$

where, $w_{j}$ is the weights of classifiers and $p_{j}$ is the prediction of
classifiers.

Fig. 7 illustrates the EL process with HPF, where the deep extracted features
are fed into the different ML algorithms, and then by applying HPF we get
three algorithms; after that, we apply EL to these filtering algorithms. In
EL, the soft and hard voting algorithms are utilized to evaluate the
performance.

Figure 7: Ensemble learning approach with HPF

After getting the estimators from HPF, we use ensemble learning with the HPF
algorithm. Where we used our estimators with hard and soft for hard and soft
voting classifiers respectively.

##  4 Experimental setup and performance metrics

The experiments are carried out on a machine running Microsoft Windows 10 Pro,
with an Intel(R) Core (TM) i3-6006U CPU running at 2.00GHz, 2000Mhz, 2 cores,
4 logical processors, and 120GB SSD, 1TB HDD, 8GB RAM and 26GB Virtual Memory.
The experiment is carried out in Anaconda Navigator using a Jupyter notebook.
To implement the suggested model, the Python programming language and various
common libraries such as pandas, NumPy, Matplotlib, Seaborn, TensorFlow,
Keras, Scikit-learn, and others are utilized.
Numerous performance metrics appraise our proposed model, including accuracy,
recall, precision, f1-score, ROC curve, confusion matrix, MSE, MAE, and RMSE.
The metrics for appraising the performance are formulated in the following:

  * 1.

The Confusion Matrix is a technique to assess machine learning classification
efficiency. It is a table-like structure with four (TP, TN, FP, FN)
combinations of predicted and actual values. Table 6 shows a confusion matrix
where the TP (True Positive) denotes a correctly anticipated positive value,
TN (True Negative) denotes a correctly projected negative value, FN (False
Positive) gives an inaccurately forecasted positive value, and FN (False
Negative) indicates an incorrectly predicted negative value. It is very useful
in helping to determine accuracy, recall, precision, f1-score, and ROC curve.

| Actual positive | Actual negative
---|---|---
Predicted positive | TP | FP
Predicted negative | FN | TN
Table 11: Confusion Matrix

  * 2.

Accuracy is the most obvious performance metric, and it is directly
proportional of correctly predicted observations to total observations.

$Accuracy=\frac{TP+TN}{TP+FP+FN+TN}$ (2)

  * 3.

The ratio between the correctly predicted positive value and the total number
of predicted positive values is defined as precision. It is depicted as:

$Precision=\frac{TP}{TP+FP}$ (3)

  * 4.

The ratio between the correctly predicted positive value and all numbers of
actual values is defined as recall. It is depicted as:

$Recall=\frac{TP}{TP+FN}$ (4)

  * 5.

The harmonic means of precision and recall values for a classification problem
is defined as f1-score. f1-score is depicted as:

$F1-score=2*\frac{(precision*recall)}{(precision+recall)}$ (5)

  * 6.

The mean absolute error (MAE) is a metric for comparing errors within related
measurements that describe a similar occurrence. It is an absolute inaccuracy
of the arithmetic average of predicted and actual values [81].

$MAE={\frac{{\sum\limits_{i=1}^{n}pred(i)-act(i)}}{n}}$ (6)

  * 7.

The mean squared error (MSE) is a statistic that calculates the mean of the
squared residuals, or the mean squared discrepancy between actual and forecast
values. Because of variability or if the predictor does not allow for data
that could generate a good prediction, the most important fact about MSE is
that it is usually typically absolutely positive (rather than zero) [44] [21].

$MSE={\frac{{\sum\limits_{i=1}^{n}(pred(i)-act(i))^{2}}}{n}}$ (7)

  * 8.

The most commonly used evaluation metric in regression problems is the root
mean square error (RMSE). It is premised on the notion that error is unbiased
and seems to have a normal distribution. Outlier attributes have quite a
massive effect on RMSE.

$RMSE=\sqrt{{\frac{{\sum\limits_{i=1}^{n}(pred(i)-act(i))^{2}}}{n}}}$ (8)

where pred(i) is the predicted value of i, act(i) is the actual value of i,
and n is the total number of values.

  * 9.

ROC curves are two dimensional plots that are often used to analyze and
examine the effectiveness of classifiers [25]. ROC graphs clearly demonstrate
a classifier’s sensitivity or specificity exchange for all potential
classification thresholds, enabling the grading and picking of classifiers
based on particular user requirements, which are commonly linked with variable
error costs and accuracy expectations [42] [79]. AUC reflects the level of
distinction while ROC is indeed a likelihood curve. It indicates how well the
model can discriminate distinct categories. On the y-axis is the true positive
rate, while on the x-axis is the false positive rate. AUC approaching 1
indicates that a predicted model is good at separability amongst class labels
whereas approaching 0 indicates a lousy predicted model. In actuality, lousy
signifies that the consequence is being mirrored [52]. It’s a strategy for
visualizing the classification’s efficiency [29]. Models with higher ROC
curves are superior classifiers [88].

  * 10.

K-fold Cross-Validation (CV) is a basic procedure for dividing a training set
into k smaller sets. The strategy for each of the k ”folds” is to utilize the
folds as training data for a model, which is then verified using the remaining
data, and the average of the values computed in the loop is then included as
an evaluation metric using k-fold cross-validation.

Our cancer detection trials were carried out using a k-fold CV with a value of
k=10. The dataset is partitioned into 80% for training and the rest of the 20%
for testing in k-fold, and the dataset gets folded into 10 separate folds,
each of which is used as a testing component throughout the folding process.
The procedure of k-fold cross-validation is depicted in Fig. 8.

Figure 8: k-fold cross-validation process

##  5 Performance Evaluation and Analysis

This section analyzes the performance of different TL methods, followed by the
analysis of our hybrid model based on the selected high-performance TL model
and voting classifier model. We have found that among all TL models MobileNet
model outperforms other models, and the soft voting classifier gives the
highest performance than the hard voting classifier.

###  5.1 Performance evaluation of lung cancer

Table 12 and Fig. 9 illustrate the performance of different TL models based on
hard and soft voting in tabular format and visual graphical bar chart
respectively. From Table 12 we can see that the average accuracy rate for hard
and soft voting classifiers is 97.66% and 98.05% respectively. Moreover in
Fig. 9 for the graphical bar chart, we can see that the graphical upper bar is
soft and lower is hard voting classifier, where for VGG16 96.9%, 97.14%; for
VGG19 96.9%, 97.14%; for MobileNet 98.81, 99.05%; for DenseNet169 98.57%,
99.05%; for DenseNet201 97.66%, 98.05% for hard and soft respectively. Hence,
it’s so clear that the soft voting classifier outperforms better than hard
voting classifier. So, we select a soft voting classifier in our proposed
model for the lung cancer dataset.

Voting Classifer | VGG16 | VGG19 | MobileNet | DenseNet169 | DenseNet201 | Average
---|---|---|---|---|---|---
Hard | 96.9 | 96.9 | 98.81 | 97.14 | 98.57 | 97.66
Soft | 97.14 | 97.14 | 99.05 | 97.86 | 99.05 | 98.05
Table 12: Accuracy performance analysis for ensemble models on lung cancer
dataset Figure 9: Accuracy performance analysis for ensemble models on lung
cancer dataset

Table 13 shows the performance of different TL models in tabular format and
insights that the MobileNet TL model gives the highest average performance
among all other TL models where the average accuracy rate for VGG16, VGG19,
MobileNet, DenseNet169, and DenseNet201 is 97.02%, 97.02%, 98.93%, 97.5%, and
98.81% respectively. Besides in Fig. 9, we can see that the graphical upper
bar chart is pointed to the MobileNet as having the highest accuracy rate
among others. So, we select MobileNet in our proposed model for the lung
cancer dataset.

TL | VGG16 | VGG19 | MobileNet | DenseNet169 | DenseNet201
---|---|---|---|---|---
Hard | 96.9 | 96.9 | 98.81 | 97.14 | 98.57
Soft | 97.14 | 97.14 | 99.05 | 97.86 | 99.05
Average | 97.02 | 97.02 | 98.93 | 97.5 | 98.81
Table 13: Accuracy performance analysis for TL models on lung cancer dataset

In Fig. 10 we can observe the performance of the MobileNet TL model. In
performance bar chart, we can visualize that the accuracy, precision,
precision, recall, f1-score are 99.05%, 99.03%,99.06%, 99.04% respectively and
in error bar chart, the MAE, MSE, RMSE are 0.95%,0.95%, 9.67% respectively. we
can insight that the accuracy rate is much higher and the error rate is much
lower. In Fig. 11 we can see the confusion matrix and ROC Curve for the
MobileNet TL model. In the Confusion Matrix, we can see that the TP rate is
30.48%, 34.29%, 34.29%; TN rate is 68.81%, 64.76%, 65.48%; FP rate is 0.48%,
0.48%, 0.0%; FN rate is 0.24%, 0.48%, 0.24% for lung squamous cell carcinoma,
lung adenocarcinomas, and lung benign tissue respectively. Here, we can
insight that the true positive and true negative rate is very high, and false
positive and false negative are very low. The large number of TP and TN than
FP and FN is a very crucial point for machine learning models to detect lung
cancer. In the ROC Curve, we can see that the AUC score is 99.96% for the soft
voting classifier and the curve is very well fitted in the graph as it is
close to 1 which is a sign of a better performance model.

(a) Performance (b) Error

Figure 10: Performance analysis for ensemble soft voting classifier for lung
cancer

(a) Confusion Matrix (b) ROC Curve

Figure 11: Confusion matrix and ROC Curve for ensemble soft voting classifier
for lung cancer

###  5.2 Performance evaluation of colon cancer

Table 14 and Fig. 12 illustrate the performance of different TL models based
on hard and soft voting in tabular format and visual graphical bar chart
respectively. From Table 14, we can see that the average accuracy rate for
hard and soft voting classifiers are 99.57% and 99.57% respectively. Moreover
in Fig. 12 the graphical bar chart, we can see that the graphical upper bar is
soft and hard voting classifiers with the same level, where 99.64%, 99.29%,
100%, 99.29%, 99.64%, 99.57% for VGG16, VGG19, MobileNet, DenseNet169, and
DenseNet201 respectively for both hard and soft voting classifier. As we know
soft voting achieves an accuracy rate based on probabilities in lieu of class
labels. So, we select a soft voting classifier in our proposed model for the
colon cancer dataset.

Voting Classifier | VGG16 | VGG19 | MobileNet | DenseNet169 | DenseNet201 | Average
---|---|---|---|---|---|---
Hard | 99.64 | 99.29 | 100 | 99.29 | 99.64 | 99.57
Soft | 99.64 | 99.29 | 100 | 99.29 | 99.64 | 99.57
Table 14: Accuracy performance analysis for ensemble models on colon cancer
dataset Figure 12: Accuracy performance analysis for ensemble models on colon
cancer dataset

Table 15 shows the performance of different TL models in tabular format and
insights that the MobileNet TL model gives the highest average performance
among all other TL models where the average accuracy rate for VGG16, VGG19,
MobileNet, DenseNet169, and DenseNet201 is 99.64%, 99.29%, 100%, 99.29%, and
99.64% respectively. Besides in Fig. 12, we can see that the graphical upper
bar chart is pointed to the MobileNet as having the highest accuracy rate
among others. So, we select MobileNet in our proposed model for the lung
cancer dataset.

TL | VGG16 | VGG19 | MobileNet | DenseNet169 | DenseNet201
---|---|---|---|---|---
Hard | 99.64 | 99.29 | 100 | 99.29 | 99.64
Soft | 99.64 | 99.29 | 100 | 99.29 | 99.64
Average | 99.64 | 99.29 | 100 | 99.29 | 99.64
Table 15: Accuracy performance analysis for TL models on colon cancer dataset

In Fig. 13, we can observe the performance of the MobileNet TL model. In the
performance bar chart, we can visualize that the accuracy, precision,
precision, recall, and f1-score are 100% for each and in the error bar chart,
the MAE, MSE, and RMSE are 0% for each. we can insight that the accuracy rate
is the highest and the error rate is zero. In Fig. 14, we can see the
confusion matrix and ROC Curve for the MobileNet TL model. In the Confusion
Matrix, we can see that the TP rate is 44.29%; TN rate is 55.71%; FP rate is
0.0%; FN rate is 0.0% for colon cancer (colon adenocarcinomas and colon benign
tissue) respectively. Here, we can observe that the true positive and true
negative rate is very high, and false positive and false negative is very low.
The large number of TP and TN than FP and FN is a very crucial point for
machine learning models to detect colon cancer. In the ROC Curve, we can see
that the AUC score is 100% for the soft voting classifier and the curve is
very well fitted in the graph as it is 1 which is a sign of a perfect
performance model.

(a) Performance (b) Error

Figure 13: Performance analysis for ensemble soft voting classifier for colon
cancer

(a) Confusion Matrix (b) ROC Curve

Figure 14: Confusion matrix and ROC Curve for ensemble soft voting classifier
for colon cancer

The accuracy rate of colon cancer is achieved at 100% which is a very
promising result ever in colon cancer detection. With the help of proper pre-
processing, feature extraction and efficient ensemble soft voting classifier
give these good results. Among all transfer learning models, the MobileNet
model gives better feature extraction than others so the accuracy label
reached the highest ever. As we already know that ensemble learning is a
multiple classifiers system [32]. It’s better to get a prediction from
multiple models than a single model, which makes a model more robust and
efficient. Hence, we have used the ensemble method to get a pleasant model
with good performance results.

###  5.3 Performance evaluation of cancer (lung and colon)

Table 16 and Fig. 15 illustrate the performance of different TL models based
on hard and soft voting in tabular format and visual graphical bar chart
respectively. From Table 16, we can see that the average accuracy rate for
hard and soft voting classifiers is 98.62% and 98.72% respectively. Moreover
in Fig. 15 for the graphical bar chart, we can see that the graphical upper
bar is soft and lower is hard voting classifier, where for VGG1698.3%, 98.4%;
for VGG19 98.0%, 98.1%; for MobileNet 99.30, 99.30%; for DenseNet169
99.3%,98.5%; for DenseNet201 99.2%, 99.3% for hard and soft respectively.
Hence, it’s so clear that the soft voting classifier outperforms better than
hard voting classifier. So, we select a soft voting classifier in our proposed
model for the (lung and colon) cancer dataset.

Voting Classifer | VGG16 | VGG19 | MobileNet | DenseNet169 | DenseNet201 | Average
---|---|---|---|---|---|---
Hard | 98.3 | 98.0 | 99.3 | 98.3 | 99.2 | 98.62
Soft | 98.4 | 98.1 | 99.3 | 98.5 | 99.3 | 98.72
Table 16: Accuracy performance analysis for ensemble models on (lung and
colon) cancer dataset Figure 15: Accuracy performance analysis for ensemble
models on (lung & cancer) cancer dataset

Table 17 shows the performance of different TL models in tabular format and
insights that the MobileNet TL model gives the highest average performance
among all other TL models where the average accuracy rate for VGG16, VGG19,
MobileNet, DenseNet169, and DenseNet201 is 98.35%, 98.05%, 99.30%, 98.40%, and
99.25% respectively. Besides in Fig. 15, we can see that the graphical upper
bar chart is pointed to the MobileNet as having the highest accuracy rate
among others. So, we select MobileNet in our proposed model for the (lung and
colon) cancer dataset.

TL | VGG16 | VGG19 | MobileNet | DenseNet169 | DenseNet201
---|---|---|---|---|---
Hard | 98.3 | 98.0 | 99.3 | 98.3 | 99.2
Soft | 98.4 | 98.1 | 99.3 | 98.5 | 99.3
Average | 98.35 | 98.05 | 99.3 | 98.4 | 99.25
Table 17: Accuracy performance analysis for TL models on cancer (lung and
colon) dataset

In Fig. 16, we can observe the performance of the MobileNet TL model. In
performance bar chart, we can visualize that the accuracy, precision,
precision, recall, f1-score are 99.30%, 99.27%,99.27%, 99.26% respectively and
in error bar chart, the MAE, MSE, RMSE are 0.7%,0.7%, 8.37% respectively. we
can insight that the accuracy rate is much higher and the error rate is much
lower. In Fig. 17, we can see the confusion matrix and ROC Curve for the
MobileNet TL model. In the Confusion Matrix, we can see that the TP rate is
19.1%, 18.1%, 21.8%, 22.1%, 18.2%; TN rate is 80.3%, 81.3%, 78.2%, 77.8%,
81.7%; FP rate is 0.5%, 0.1%, 0.0%, 0.0%, 0.1%; FN rate is 0.1%, 0.5%, 0.0%,
0.1%, 0.0% for lung squamous cell carcinoma, lung adenocarcinomas, lung
benign, colon benign, and colon adenocarcinomas respectively. Here, we can
observe that the true positive and true negative rate is very high, and false
positive and false negative is very low. The large number of TP and TN than FP
and FN is a very crucial point for machine learning models to detect lung
cancer. In the ROC Curve, we can see that the AUC score is 99.99% for the soft
voting classifier and the curve is very well fitted in the graph as it is
close to 1 which is a sign of a better performance model.

(a) Performance (b) Error

Figure 16: Performance analysis for ensemble soft voting classifier for (lung
and colon) cancer

(a) Confusion Matrix (b) ROC Curve

Figure 17: Confusion matrix and ROC Curve for ensemble soft voting classifier
for (lung and colon) cancer

###  5.4 Discussion

In our proposed model, we have evaluated our model using deep feature
extraction and ensemble learning approach based on high-performance filtering
and locating the best TL models for feature extraction as well as ensemble
models providing a better accuracy rate. The proposed method is compared with
the methods in other papers and significantly achieves the highest accuracy
than others. The comparison study of our proposed model with others is
illustrated in tabular format in Table 18. In comparison, the dataset that we
have utilized and compared with others is the same dataset (HPI LC25000) and
the number of images employing the methods is slightly different. The similar
datasets with a tiny variation of datasets make the comparison as a fair
comparison to estimate the performance.

In our experiments, we achieve the highest performance accuracy rate: lung
dataset 99.05%; colon dataset 100%; (lung and colon) dataset 99.30% which
outperforms the others. Hence, Among all of the preceding in both lung and
colon cancer detection, our proposed hybrid model significantly achieves the
highest accuracy.

SI. No. | Author | Approach | Dataset | Accuracy (In %)
---|---|---|---|---
1 | [33] | CNN | HPI Lung [11] | 97.20
2 | [49] | SCNN | 97.89
3 | [69] | CAD | 98.67
4 | Our proposed model | Hybrid Model | 99.05
5 | [74] |  | CNN (MaxPool)
---
CNN (AvgPool)
MobileNetV2
HPI Colon [11] |  | 97.49
---
95.48
99.67
6 | [46] | MFF-CNN | 96
7 | [49] | SCNN | 96.61
8 | [63] | CNN | 99.6
9 | [87] | MA ColonNET | 99.75
10 | Our proposed model | Hybrid Model | 100
11 | [50] | 2D FW+ CNN | HPI Lung and Colon [11] | 96.33
12 | [72] | CNN | 93
13 | [4] | DHS-CapsNet | 99.23
14 | Our proposed model | Hybrid Model | 99.30

Table 18: Comparison analysis of lung and colon cancer

###  5.5 Speed and complexity analysis

The prediction speed is the time needs to predict cancer and the resource
usage cost is simply the complexity of the ML model. The time and space
complexity, time requires to execute an operation and the space requires to
store data, are very important to determine the efficiency of any algorithm
[90] [2]. In the following section, we have mentioned the prediction speed and
complexity analysis. The diagnosis of lung and colon cancer has an overall
computational complexity of O(n²). In our method, we take the estimators from
the HPF process that requires O(n²) and build an ensemble learning method that
consumes O(n) time to complete, so the overall complexity is O(n²) + O(n)
$\sim$ O(n²). The other ML models such as RF, SVM, LR, MLP, XGB, and LGB take
O(tlogn), O(n²), O(m), O(n²), O(td), and O(tl) respectively, where n is the
number of data points, m is the number of features, t denotes the number of
trees, l is the level of the trees, and d is the depth of the trees. On the
other hand, diagnosing lung and colon cancer has an overall space complexity
of O(n²). In our method, we take the estimators from the HPF process that
requires O(n²) and build an ensemble learning method that consumes O(n) time
to complete, so the overall complexity is O(n²) + O(n) $\sim$ O(n²). The other
ML models such as RF, SVM, LR, MLP, XGB, and LGB take O(et), O(km), O(m),
O(n²), O(et+g), and O(et+l) respectively, where n is the number of data
points, m is the number of features, t denotes the number of trees, e is the
number of nodes, l is the level of the trees, and g is the output values for
each leaf in decision trees. The analysis of time and space complexity is
shown in Table 19.

Method | RF | SVM | LR | MLP | XGB | LGB | HPF | EL | Proposed Model
---|---|---|---|---|---|---|---|---|---
Time Complexity | O(tlogn) | O(n²) | O(m) | O(n²) | O(td) | O(tl) | O(n²) | O(n) | O(n²)
Space Complexity | O(et) | O(km) | O(m) | O(n²) | O(et+g) | O(et+g) | O(n²) | O(n) | O(n²)
Table 19: The time and space complexity of the ML Models

Furthermore, we estimated the prediction time (sec) on the system having 4
cores CPU, 16GB RAM, and 1 GPU employed by our proposed model while working
with the 420 (lung), 280 (colon), and 1000 (lung and colon) testing images.
For lung, colon, and (lung and colon) cancer, our approach encloses the
prediction times of 2s, 0s, and 14s, respectively. The prediction time grows
as the number of testing images grows. The analysis of prediction time is
shown in Table 20.

Method | RF | SVM | LR | MLP | XGB | LGB | Proposed Model
---|---|---|---|---|---|---|---
Prediction Time (sec) | Lung Dataset | 0 | 2 | 0 | 0 | 0 | 0 | 2
Colon Dataset | 0 | 0 | 0 | 0 | 0 | 0 | 0
Lung and Colon Dataset | 0 | 14 | 0 | 0 | 0 | 0 | 14

Table 20: The prediction time (sec) of the ML Models

##  6 Conclusion

This paper presented a hybrid model for lung and colon cancer detection that
integrates pre-processing, k-fold (10 fold) cross-validation, feature
extraction using several TL models, devoting ML algorithms, picking the best
algorithms using HPF and suit into ensemble learning models. After that select
the best ensemble voting classifier model and most suitable feature extraction
TL model. Moreover, we employed five TL models in feature extraction such as
VGG16, VGG19, MobileNet, DenseNet169, and DenseNet201. At the same time, for
evaluating the performance using ML algorithms, we used six popular algorithms
namely RF, SVM, LR, MLP, XGB, and LGB. This extensive development has been
evaluated by a set of performance metrics such as accuracy, recall, precision,
f1-score, ROC Curve, MAE, MSE, and RMSE. After considering our experiments, we
choose the MobileNet model for Feature extraction among all TL models, and an
ensemble soft voting classifier from two ensemble models to evaluate our
performance. Our experiment uses the LC25000 lung and colon histopathological
image datasets and finds that our hybrid model is best suited for diagnosing
lung and colon cancer. In terms of performance metrics, our suggested model
performed admirably and outperformed other techniques. The accuracy rate for
lung cancer detection is 99.05%; for colon cancer, it is 100%, and for lung
and colon cancer, it is 99.30%. We believe our developed model could be
applicable in clinics for the automated diagnosis of lung and colon cancers.
Even though the architecture delivers greater accuracy, further sophisticated
work on image pre-processing and the proposed model would make the task more
adequate. A good feature extraction leads to producing better performance
results if the image is clear and sharp to extract features is an inadequacy
in this paper. In the future, we will go to explore the newly available lung
and colon cancer dataset with efficient preprocessing to build a deep learning
approach to enhance the performance to detect lung and colon cancer.

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