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ArticlesGastrointestinal Diseases Recognition: A Framework of Deep Neural Network and Improved Moth-Crow Optimization with DCCA Fusion
  • Muhammad Attique Khan1, Khan Muhammad2, Shui-Hua Wang3, Shtwai Alsubai4, Adel Binbusayyis4, Abdullah Alqahtani4, Arnab Majumdar5, Orawit Thinnukool6,*

Human-centric Computing and Information Sciences volume 12, Article number: 25 (2022)
Cite this article 1 Accesses
https://doi.org/10.22967/HCIS.2022.12.025

Abstract

Wireless capsule endoscopy (WCE), the most efficient technology, is used in the endoscopic department for the examination of gastrointestinal (GI) diseases such as a poly and ulcer. WCE generates thousands of frames for a single patient’s procedure, and the manual examination is time-consuming and exhausting. In the WCE frames, computerized techniques make the manual inspection process easier. Deep learning has been used by researchers to introduce a variety of techniques for the classification of GI diseases. Some of them have concentrated on ulcer and bleeding classification, while others have classified ulcers, polyps, and bleeding. In this paper, we proposed a deep learning and Moth-Crow optimization-based method for GI disease classification. There are a few key steps in the proposed framework. Initially, the contrast of the original images is increased, and three operations based on data augmentations are performed. Then, using transfer learning, two pre-trained deep learning models are fine-tuned and trained on GI disease images. Features are extracted from the middle layers using both fine-tuned deep learning models (average pooling). On both extracted deep feature vectors, a hybrid Crow-Moth optimization algorithm is proposed and applied. The resultant selected feature vectors are later fused using the distance-canonical correlation (D-CCA) approach. For classifying GI diseases, the final fused vector features are classified using machine learning algorithms. The experiments are carried out on three publicly available datasets titled CUI Wah WCE imaging, Kvasir-v1, and Kvasir-v2, providing improved accuracy with less computational time compared with recent techniques.


Keywords

Stomach Cancer, Wireless Capsule Endoscopy, Contrast Enhancement, Deep Learning, Optimization, Features Fusion


Introduction

A lot of research is being carried out by researchers in the field of medical imaging by utilizing computer vision and deep learning (DL) on different types of imaging technology such as magnetic resonance imaging (MRI), computed tomography (CT) [1], dermoscopy, X-ray [2], and capsule endoscopy [3]. A common type of cancer that affects both men and women is colorectal cancer [4]. These types of cancers can be a polyp, bleeding, or ulcer. Stomach disease affects approximately 3.6 million children annually [5]. In the United States, the number of registered colorectal cancer cases since 2015 tallies 1.32 million. The total number of bowl infection cases tallies 1.6 million and 200,000 new cases are added each year. Early-stage diagnosis is very difficult due to a high mortality rate. In 2021, the number of reported stomach cancer cases tallied 149,500, while the numbers of deaths tallied 52,980 [6].
Some infections can be linked to colorectal cancer such as short bowl and hemorrhoids. The diagnosis of these infections can be done by using colonoscopy techniques [7]. The drawback of this method is its high time consumption and a limited number of specialists available [3]. Moreover, this method is not suitable for detecting small bowls due to their complex build. This problem was addressed by introducing a new technique called wireless capsule endoscopy (WCE) [8]. WCE is a widely used method for detecting gastrointestinal (GI) diseases. In this method, a tiny camera with a diameter of 11 mm × 30 mm is used to capture the region of GI. The whole procedure may take more than 2 hours [9]. After capturing, all frames are compressed by utilizing the JPEG technique. In WCE, there is no need for an external wire and patients are asked to swallow the camera to record the video. After that, the radio telemetry method is used to transfer the videos to an external recorder. An array based on eight aerials is attached to the patient and a capsule in GI tract is located. This method can detect diseases and perform a small bowl diagnosis, and so it is popular in hospitals. Approximately 1 million people are successfully treated by this method in the last year [10]. However, this method has a few limitations such as time consumption and a lack of experts. The main concern with this method is the time constraint because it takes a long time for a manual diagnosis. Also, an ulcer in the WCE images is not clearly visible due to the low contrast [11]. Therefore, there is a chance that a physician may miss the region of ulcer during the detection process. Furthermore, there is another problem that occurs during a diagnosis with the naked eyes with regard to similarity of color, texture, and variations of the shape [12].
Therefore, researchers introduced various CV techniques for the diagnosis and classification of medical infections such as stomach cancers [13], skin cancers [14], and brain tumors. These techniques are based on some basic steps such as increasing the contrast and removing the noise from the original image, segmenting infected regions in the image, extracting important features of each image, selecting the best features, and finally classifying them into relevant classes. Contrast enhancement is an important step of a computerized method. The main purpose of this step is to improve the intensity range of an infected region to get better segmentation accuracy and extract relevant features [15]. In the segmentation part, infected regions are detected through several techniques such as saliency based [16], and named a few more [17]. The resultant images of this step are passed to the next step for feature extraction; however, this step has several challenges (i.e., change in shape of infected lesion, similarity in color of healthy and infected parts, presence of an infected region on the border) that reduce the segmentation accuracy. The reduction in segmentation accuracy later produces a misclassification of a disease into the relevant class.
Recently, a deep convolutional neural network (CNN) has shown improved performance for both detection and classification of medical infections [1820]. A CNN is a form of DL that includes several layers such as convolutional, ReLu, fully connected, and pooling. In a CNN model, the raw images are normally processed for features extraction and classification. Compared to the classical techniques, the CNN-based techniques produce much better and reliable results. Several DL techniques are utilized recently for automated recognition of multiple stomach diseases, as illustrated in Fig. 1 [21]. These methods utilized the pre-trained CNN models for features extraction that are later employed for features optimization. The pre-trained CNN models are trained through transfer learning (TL) due to the shortage of memory and time.

Fig. 1. Sample WCE images of multiple stomach diseases collected from Kvasir-v1 [21].


As an example, Ayyaz et al. [22] presented a hybrid CNN-based approach for stomach infection detection and classification. For feature extraction, they used several CNN pre-trained models, including VGG and AlexNet. Later on, they used a genetic algorithm to select the best features, which were then classified using machine learning methods. By the same token, Lee at al. [23] utilized DL models such as ResNet-50, VGG16, and Inception-V3 for classifying normal and ulcer GI images. In this method, Resnet-50 outperformed the rest of the deep networks. Khan et al. [24] introduced a saliency-based method to segment the GI infections, whereas a DL architecture is used for classification. They used a YIQ color space along with an HSI color space that is later feed to a contour-based approach for segmentation. Suman et al. [25] used several color spaces for feature extraction such as CMYK, LAB, YUV, RGB, XYZ, and HSV for non-ulcer and ulcer detections. These feature vectors are fused by utilizing a cross-correlation method, and a final classification is done by using support vector machine (SVM), attaining an accuracy of 97.89%. Yuan et al. [26] introduced an automated method for detecting an ulcer from the WCE frames. Initially, a saliency approach based on super pixel is implemented to draw an ulcer region boundary. Next, each level texture and color attributes are computed and fused to get the final map of saliency. After that, a saliency max-pooling (SMP) technique is introduced and merged with locality-constrained linear coding (LLC) to attain a recognition rate of 92.65%. Rustam et al. [27] proposed a bleedy image recognizer (BIR) DL architecture for classifying bleeding infected frames. They trained with using BIR and two custom deep models. They used 1,650 WCE images in the evaluation process and achieved an improved accuracy. The primary goal of this study was to perform an automatic analysis of WCE bleeding images. Jain et al. [28] presented an attention-based DL architecture for stomach disease classification and localization from WCE images. Initially, they performed an efficient CNN-based classification of stomach diseases. Later on, they combined Grad-CAM++ and a custom SegNet for the localization of infected regions. The presented method is evaluated on a KID dataset and achieved an improved accuracy. Lan and Ye [29] introduced a combination of an unsupervised DL method for WCE video summarization. They used several networks such as LSTM, autoencoder to name a few more and then performed summarization. The main purpose of this work was to help doctors in the analysis of the entire WCE video. Naz et al. [30] introduced a hybrid sequential framework for classifying stomach diseases from WCE images. They initially performed contrast enhancement through filtering techniques, followed by feature extraction using a hand-engineered method and VGG. Finally, a serial-based fusion is used, while the classification is performed. The main purpose of this work was to improve the current classification accuracy of stomach diseases. Several other techniques were also presented such as graph convolutional based [31], CNN-batch normalization [32], to name a few more [33].
The features extracted from a single deep network extract a single characteristic; however, recent work has shown a decrease in accuracy when dealing with complex images [34]. Furthermore, the features extracted from the pre-trained models contain a significant amount of irrelevant and redundant information [13]. To deal with the redundant information, the researchers used several fusion and feature selection techniques, which thereby improved the accuracy. However, they were still confronted with the issue of computational time and room for improvement in the accuracy. We proposed a new automated end-to-end framework for stomach disease classification using a DL and hybrid moth-crow optimization algorithms in this article. The following are the major contributions of this work:

A contrast stretching technique based on the maximum intensity value of the infected region and a combination of local-global information are proposed.

For feature extraction, two pre-trained models, called MobileNet-V2 and NasNet Mobile, are fine-tuned and trained with using TL.

Instead of deep layers, deep learning features are extracted from average pooling layers and refined, using an entropy-based function. The features are then combined with the help of a serial-based average threshold function.

An amalgam crow-moth optimization algorithm is proposed based on the cross-entropy loss function for the best DL feature selection.


The remainder of the paper is structured as follows: Section 2 provides a detailed mathematical formulation of the proposed DL and crow-moth flame optimization algorithms. The experimental results are presented in Section 4, which follows the conclusion in Section 5 with our key findings.


The Proposed Methodology

The proposed DL and hybrid crow-moth optimization algorithm-based frameworks consist of a few important steps, as illustrated in Fig. 2. The contrast of the original images is increased, and three operation-based data augmentations are performed. After that, two pre-trained DL models are fine-tuned and trained on GI disease images using TL. By employing both fine-tuned DL models, features are extracted from the middle layers (average pooling). A hybrid crow-moth optimization algorithm is proposed and applied on both extracted deep feature vectors that are later fused using the distance-canonical correlation (D-CCA) approach. The final fused vector features are classified using machine learning algorithms for classifying GI diseases.

Fig. 2. The proposed framework of GI diseases classification using deep learning and moth-crow-based features optimization.


Contrast Enhancement and Augmentation
Let $\tilde Δ$ be a database having three datasets of WCE images. Suppose $ϕ(i,j)$ is a WCE image of dimensions $M×N×K$, where $M=N=256$ and $K=3$. The $K=3$ represents that the nature of input image is RGB. The contrast of each image is computed by the following mathematical formulation expressed as:

$ϕ_{c1}(i,j)=\tilde B_t+\tilde T_h$(1)

$\tilde B_t=B_t(ϕ(i,j)⋅S)+Ct$(2)

$\tilde T_h=T_h(ϕ(i,j)∘S)+Ct$(3)

$\tilde ϕ ̃_c(i,j)=(ϕ_c1 (i,j)-ϕ(i,j))*\tilde B_t$(4)

where, $ϕ_{c1}(i,j)$ is initial contrast enhanced resultant image, $\tilde B_t$ is bottom hat transformed values, $\tilde T_h$ denotes top-hat transformed values, $\tilde ϕ_c(i,j)$ is updated contrast enhanced values, Ct is constant of value 1, and S is a structuring element value of 11. Two operators are also applied such as opening and closing denoted by $(∘)$ and $(⋅)$. The Gaussian function is applied on the updated contrast image $\tilde ϕ_c(i,j)$ to remove the noisy pixels. Mathematically, it is defined as follows:

$G=\frac{1}{2πσ} e^{\frac{-1}{2}(\frac{\tilde ϕ_c-Mean}{σ}}$(5)

$Mean= \frac{1}{MN} \displaystyle\sum_{i=1}^M \displaystyle\sum_{j=1}^N(\tilde ϕ_c(i,j))$(6)

$σ=\sqrt {E(\tilde ϕ_c(i,j))-E(E\tilde ϕ_c(i,j))^2 }$(7)

$\tilde ϕ_G(i,j)=G(\tilde ϕ_c(i,j))$(8)

where, $G$ denotes the Gaussian function, σ is a standard deviation, and $\tilde ϕ_G$ is Gaussian updated image. After that, the minimum and maximum intensity values are computed denoted by α from $\tilde ϕ_G$ and the Gaussian image is updated and expressed as follows:

$α= \frac{max⁡(\tilde ϕ_G(i,j)) + min(\tilde ϕ_G(i,j)}{2}$(9)

$\tilde ϕ_{\tilde G}(i,j)=\tilde ϕ_G(i,j)×α$(10)

where, $\tilde ϕ_{\tilde G}(i,j)$ is updated Gaussian image. This image is divided into three different channels and histogram of each channel is computed. Based on the histograms, the higher frequency pixel value is computed that is embedded in the threshold functions expressed as follows:

$Ch_k(x,y)= \tilde ϕ_{\tilde G}(M,N,k), k=1,2,3$(11)

$Hist_k=\displaystyle\sum_{k=1}^3 H(Ch_k)$(12)

where, $H$ is the histogram of each channel. Based on $Hist_k$, three probability values $p_1, p_2$, and $p_3$ are obtained and are individually put in the tumor region brightness increasing function expressed as follows:

$Tr=\begin{cases} (Ch_k for Ch_k(x,y)≤p_{\breve k} \cr Not Updated Otherwise) \end{cases} $, $\breve k∈(1,2,3)$(13)

This function describes that if the pixel value of each channel ($\breve k$) is less than or equal to the corresponding threshold value, and then the pixel value is updated with that probability value, otherwise the same pixel value will be considered. After that, all three resultant channels are combined and multiplied with a harmonic mean value. The new updated image is obtained as follows:

$\tilde ϕ_{cn}(i,j)=ψ(3,Ch_k ), k=1,2,3$(14)

$\tilde ϕ_{fI} (i,j)=\tilde ϕ_{cn} (i,j)×\widehat{HM}$(15)

where, $\widehat{HM}$ is a harmonic mean value defined as $\widehat{HM}=\frac{N}{∑(\frac{1}{\tilde ϕ_{cn}})}, \tilde ϕ_{cn}$ is a concatenated image of three channels, and $\tilde ϕ_{fI}(i,j)$ is the final contrast enhanced image. Visually, the resultant image is shown in Fig. 3. After this, three operations (horizontal flip, vertical flip, and rotate 90) are performed two times on all images of each dataset to increase the training images.

Fig. 3. Proposed tumor contrast enhancement using WCE images.


Transfer Learning
TL is a technique to reuse a pre-trained deep CNN model for another task. As shown in Fig. 4, it is described that the originally pre-trained DL models are trained on an ImageNet dataset having 1,000 object classes. Through knowledge transferring, the fine-tuned models are trained on Stomach dataset without having a maximum 8 GI diseases. This shows that the target data is less than the source data. Hence, the TL main purpose is to re-train a fine-tuned DL model on a smaller dataset. Mathematically, we can define this process shown as follows.
A source domain is provided $S_{dom}={(u_1^S,v_1^S ),…..,(u_i^S,v_i^S ),……(u_n^S,v_n^S ) }$, where $(u_n^S,v_n^S )∈R$; with a specific learning objective, $S_l$, and target domain $T_{dom}={(u_1^t,v_1^t ),…..,(u_i^t,v_i^t ),……(u_m^t,v_m^t ) }$, along with learning task $T_{dom},(u_n^t,v_n^t )∈R$. The size of the training data is $((m,n)|n≪m)$, and the labels are $Z_1^D$ and $Z_1^T$. TL's major role is to enhance the target function $T_{dom}$ learning ability and leveraging the information from the source $S_{dom}$ and target $T_{dom}$.

Deep Features Extraction
MobileNet-V2 [35] is a new pre-trained DL light weight model used for classification. Compared to the V1 version [36], the MobileNet-V2 has a better capability to address the problem of gradientvanishing and due to the addition of an inverted residual block and linear bottleneck frame. The new features of MobileNet-V2 are the addition of expansion layer of 1×1 convolution and expending the channels before going to the depth wise convolution operation. This network is known as the “DagNetwork,” and accepts an input of dimensions 224×224×3. This network includes a total of 154 layers and 163×2 connections. Originally, this network is trained on an ImageNet dataset and the output of the last layer has 1,000 object classes. The loss function named cross-entropy is employed for classification. In this work, fine-tuning is performed to reuse this model for feature extraction of GI WCE images. To accomplish this, the last three layers are removed with three new layers added known as New FC, Softmax, and Classification Output. Furthermore, we have only frozen the first 50 layers and retrained the remaining layers on the target dataset using TL. Following the fine-tuning of the model, features are extracted from the global average pooling layer, andthe dimension of extracted features on this layer is N×1280.
NasNet Mobile [37] is a DAGNet CNN architecture, consisting of basic building blocks that are optimized through reinforcement learning. Each cell consists of several layers such as convolutions, pooling, and recurrent as per the size of the network. This network has 5.3 M parameters and a total of 12 cells. The first layer of this network called the “input layer” accepts an input of dimensions 224×224×3. The total layers in this network are 913 with 1072×2 connections. We replaced the last three layers with the New FC, Softmax, and Classification Output layers during the fine-tuning process. Except for the first input layer, the first 500 layers are frozen, and the rest are retrained on GI WCE datasets. The training is done with TL, along with a learning rate of 0.001, mini batch size of 64, 100 epochs, and an ADAM optimization method. Other hyperparameter values include a dropout factor of 0.5, weight decay of $4e^{-3}$, and norm decay of 0.8. Following the training of a fine-tuned model, features are extracted from the global average pooling layer, yielding a feature vector of dimensionsN×1056.

Fig. 4. Transfer learning based retrained a pre-trained deep learning model for GI disease classification.


Moth-Crow Features Optimization
In this work, a moth-crow optimization algorithm is utilized and modified through the Renyientropy (RE) activation function and single layered feed forward neural network. Consider, we have two feature vectors denoted by $\tilde ψ_1$ and $\tilde ψ_2$ having dimensions N×1280 and N×1056, respectively. The crow search algorithm [38] is applied on each vector separately and an RE activation function is added at the end. Features passed from the activation function are considered for the fitness calculation and process is continued for the initialized iterations. After that, a best crow search best feature vector is obtained and further passed to the moth flame optimization algorithm [39]. Similar to the crow search, RE activation function is selected and checked through fitness function for final features selection.
Consider, $C_n$ crows that means flock size is $C_n$. At the $i_{th}$ iteration, the current location of crow $C$ may be described as vector $A^{l,m}$.

$A^{l,m}=[A_1^{l,m},A_2^{l,m},A_3^{l,m},…….,A_n^{l,m} ]$(16)

Each crow has its own memory that stores information about where it hides its food. $P_{k,i}$ represents the Crow $C$ food hiding position at the $i_{th}$ iteration. Crow $C$ has discovered the finest place yet. Crows visit and investigate various sites in order to find the better food hiding sites. For this purpose, the position mechanism is selected. Assume that during the $i_{th}$ iteration, crow $C$ must go to its food hiding place $P_{k,i}$. At the same moment (iteration), crow $D$ chooses to pursue crow $k$ in order to get access to crow $C$'s food hiding spot. In this circumstance, two update conditions are selected:

$x^{D,i+1}=x^{D,i}+D_{rand}×L^{D,i}×(P_{k,i}-x^{D,i})$(17)

where, $D_{rand}$ denotes the random numbers between 0 and 1 and $L^{D,i}$ indicates the crow Dat $i^{th}$ iteration’s flight length. The $L^{D,i}$ has a substantial impact on the algorithm's capacity to search. Hence, the lower $L$ values favor local search, whereas larger $L$ values favor global search. The second update equation is defined as follows:

$x^{D,i+1}= \begin{cases} x^{D,i}+D_{rand}×L^{D,i} \cr ×(P_{k,i}-x^{D,i}) r_D ≥ aprob^{C,i} \cr a random location \mkern18mu otherwise \end{cases} $(18)

where $r_D$ donates uniformly distributed random number with range between 0 and 1and $aprob^{C,i}$ donates the crow $D$ awareness probability $ati^{th}$ iteration. We selected Eq. (18) update criteria and compute the RE value as follows:

$REv=Et(x^{D,i+1})$(19)

$Et=- log \displaystyle\sum_{i=1}^{NT} P_i^2 $(20)

where, $REv$ denotes the RE value, Et is RE function, $x^{D,i+1}$ is input initial crow search feature vector, NT denotes the total number of features, and $P$ is the probability of each value. Based on the REv, an activation function is proposed that selects the features for further processing expressedas follows:

$At=\begin{cases} (\tilde x^{D,i+1} \mkern18mu for \mkern18mu x^{D,i+1}≥REv \cr Remove, \mkern18mu Otherwise) \end{cases} $(21)

The selected features $\tilde x^{D,i+1}$ from this function are evaluated through single layered feedforward neural network (SLFFNN) [40] fitness function. The MSER is selected as a loss function of the fitness function. This process is executed 100 times and after that, a possible best feature vector is obtained that is further refined through moth flame algorithm [39]. The moth flame works based on the following three steps of creating the population, updating the positions, and updating the final amount of flame. Initially, the group of moths can be expressed as follows:

pyo(22)

where, $B∈\tilde x, m$ denotes the number of moths, and $z$ denotes the number of dimensions. The fitness value of each moth is stored in the following array manner:

pyo(23)

After that, the position of the moths is updated to get the global best solutions. To find the global best solution of the optimization challenge, the following function is selected:

$MFO =(R,S,T)$(24)

where $R$ denotes the initial moths’ random positions defined by $(R:P→{P,OP}), S$ denotes the movement of moths in the search space defined by $(S:P→P)$, and $T$ denotes the complete search space defined by $(T:P→True,False)$. The P is utilized to implement the random distribution and is defined as follows:

$P(i,k)=(u(i)-l(k)*r()+lb(i))$(25)

$U(P_i,Q_j )=H_i.e^{a1c}.cos⁡(2πt)+Q_j$(26)

where $l$ andu are the variable’s lower and upper limits, respectively. The gap between the $i_{th}$ moth and the $j_{th}$ flame is referred to as $H_i i.e., (H_i=|Q_j-P_i |)$. The symbol a1 is fixed for determining the logarithmic spiral’s form, where c is an integer between -1 and 1. The moths update the positions until they get the local optima and the optimal solutions have been retained in each iteration. Finally, the selected flames (features) are obtained by the following equation shown as:

$F_n^{i+1}=round(Max-*\frac {Max-I}{V}$(27)

where, Max denotes the highest possible amount of flames, I denotes the number of iterations currently in progress, and V denotes the amount of total iterations. The selected flames F_n^{i+1} are passed to the Eqs. (22)–(24) and their fitness is checked through SLFFNN. This above entire process is applied on both deep extracted feature vectors through which two best selected vectors are obtained in the output with dimensionsN×752 and N×660.

Distance Canonical Correlation based Fusion
Consider that we have two selected feature vectors $A_1$ and $A_2$ having dimensions N×752 and N×660, respectively. Initially, CCA [41] based fusion is performed and then the distance among pair of features is computed for the final fusion.CCA looks for transforming the two variates to such transformed variates, except for the maximum relationship across the two features vectors. In the two given feature vectors $A_1∈R^(i×m)$ and $A_2∈R^(i×n)$, CCA finds the linear combinations A1U1 and A2U2 that maximize the feature vectors across the pairwise correlation. The $Z$1 and $Z_2∈R^(i×n), c≤min(rank(A1, A2))$, these are called recognized variates and $U_1∈R^(m×c)$ and $U_2∈R^(n×c)$ and these are the canonical coefficients vectors. The first pair of canonical coefficient vectors $u_1^{(1)}is found bythedeflationary approachmethod. The linear combinations of two feature vectors are maximized and expressed as follows:

$max_{u_1^{(1)},u_2^{(1)}} corr (A_1 u_1^{(1)},A_2 u_2^{(1 )})$(28)

Based on this equation, the maximum combination is computed among the features. By considering the rest of the steps in CCA, we skipped all the others and selected the distance formula. Through the distance formula, features are fused based on the minimum distance value shown as follows:

$Dis=\sqrt {(f_{i+1}-f_i)^2-(f_{j+1}-f_j )^2 }$(29)

Based on this formula, the distance is computed among each feature, and only those feature pairs are considered whose distance is a minimum. The final fused vector is passed to machine learning classifiers for the final classification results. A few labeled results of the proposed framework are illustrated in Fig. 5.

Fig. 5. Sample labeled GI disease prediction results of the proposed framework.


Results and Discussion

The proposed framework is evaluated on three publically available WCE images datasets having multiple GI diseases. The selected datasets are CUI Wah WCE images [34], Kvasir-v1, and Kvasir-v2 [21]. The 50% of the images of each dataset are utilized for the training of the models, while 50% is employed for the testing process. In this work, the cross-validation value is set to 10-fold for the entire experimental process. The hype parameters are selected for the training of DL models based on a learning rate of 0.001, mini batch size of 64, 100 epochs, optimization method through ADAM, dropout factor of 0.5, weight decay of $4e^{-3}$, and norm decay is 0.8. The sigmoid activation function is employed for features extraction, whereas the action loss is a cross-entropy max. Several classification methods have been employed for the evaluation applying a fine tree, weighted k-nearest neighbors (KNN), ensemble baggage tree, and multiclass SVM. The performance of each classifier is computed based on accuracy and computational time. The entire framework is simulated on MATLAB2021b equipped with a personal computer and 8GB of graphics card and 32GB of RAM.

Results
The proposed framework is evaluated based on the five middle step experiments consisting of (1) features extraction from a fine-tuned MobileNet-V2; (2) features extraction from a fine-tuned NasNet mobile; (3) crow-moth flame modified optimization algorithm applied on MobileNet-V2 features; (4) crow-moth flame modified optimization algorithm applied on NasNet mobile features, and (5) fusion of best features using a D-CCA-based approach. The results are computed for each step using all datasets to analyze their importance in the proposed framework, accordingly.

Table 1. Classification results of the proposed framework on CUI WCE images dataset
Classifier Feature Performance measure
MobileNet NasNet Mobile CMFO-MobileNet CMFO - NasNet Mobile Fusion Accuracy (%) Time (s)
Fine tree 89.64 336.8834
86.52 259.4367
92.93 154.9987
90.64 136.6625
94.15 107.2105
Weighted KNN 91.46 362.7745
88.27 310.32267
94.18 194.7725
91.59 180.0024
96.52 145.1178
Ensemble baggage tree 91.95 405.1178
87.47 367.9935
94.85 226.7843
90.43 210.5776
96.78 163.0078
MCSVM 94.65 276.7734
92.87 251.173
97.64 142.0783
96.79 128.7723
99.42 103.8901
The best results are given in bold.

CUI WCE Dataset Results: Table 1 shows the outcomes of the proposed framework on the CUI WCE dataset. This table displays the classification accuracy of each step for the selected classifiers. The fine tree achieved the highest accuracy of 94.15% and shortest execution time of 107.2105 seconds for the D-CCA-based selected features fusion. The weighted KNN achieved the highest accuracy of 96.52% for the D-CCA fusion, with the shortest time of 145.1178 seconds. With a computational time of 163.0078 seconds, the ensemble tree achieved the highest accuracy of 96.78%. For the fusion process, the MCSVM classifier had a higher accuracy of 99.42%. This classifier’s best execution time was 103.8901 seconds. Fig. 6 also illustrates the MCSVM (fusion) confusion matrix. According to the results, the fusion process produces superior results; however, selecting the best features improves classification accuracy when compared to fine-tuned model features. Furthermore, the selection process reduces the computational time that was later improved in the fusion step. Fig. 7 illustrates the overall time of this dataset, which demonstrates the strength of the proposed framework.

Fig. 6. Confusion matrix of MCSVM for CUI WCE dataset.


Fig. 7. Comparison of middle steps of the proposed framework based on computational time.


Kvasir-v1 Dataset Results: Table 2 presents the classification results of the proposed framework on the Kvasir-v1 dataset. Similar to the CUI Wah WCE dataset, this dataset was also evaluated on middle steps for each selected classifier. The fine tree achieved the highest accuracy of 92.57% and the minimum testing time of 91.9465 seconds for the D-CCA-based selected features fusion. The weighted KNN classifier obtained the best accuracy of 93.28% for the fusion process with the shortest time of 106.7678 seconds. Performance of the ensemble tree is not improved than the previous classifiers, with this classifier achieving an accuracy of 92.89%. For the D-CCA-based features fusion, the MCSVM classifier had a higher accuracy of 97.85% on this dataset. The shortest time of this classifier was 71.0315 seconds that was smaller than the rest of the classifiers. The confusion matrix of MCSVM is also presented as shown in Fig. 8. This figure illustrated the true prediction rate of each class. According to the results provided in this Table, it is observed that the initial fine-tuned models’ accuracy is improved after employing the features selection algorithm. Also, it is noteworthy that the selection process reduced the overall testing time of the proposed framework. The performance of MCSVM is better than the rest of the classifiers for all middle and fusion steps. Fig. 9 illustrated the time plot of this dataset and shows the strong point of the feature selection and D-CCA fusion.

Table 2. Classification results of the proposed framework on Kvasir-v1 dataset
Classifier Feature Performance measure
MobileNet NasNet Mobile HMFO-MobileNet HMFO- NasNet Mob Fusion Accuracy (%) Time (s)
Fine tree 87.43 192.5783
85.29 180.7443
90.75 126.5521
88.49 116.0078
92.57 91.9465
Weighted KNN 89.14 212.5976
86.97 190.7654
92.52 154.5895
90.3 140.2574
93.28 106.7678
Ensemble baggage tree 88.5 275.7158
85.71 207.5754
90.5 176.3113
89.83 141.6432
92.89 123.7386
MCSVM 91.98 176.7832
90.54 157.8594
94.24 102.3899
93.9 88.3243
97.85 71.0315
The best results are given in bold.

Fig. 8. Confusion matrix of the proposed framework for Kvasir-v1 dataset.


Fig. 9. Comparison of middle steps of the proposed framework based on computational time on Kvasir-v1 dataset.


Table 3. Our framework’s classification results on the Kvasir-v2 dataset
Classifier Feature Performance measure
MobileNet NasNet Mobile HMFO-MobileNet HMFO- NasNet Mob Fusion Accuracy (%) Time (s)
Fine tree 87.43 260.6804
85.29 287.9583
90.75 168.8992
88.49 152.7328
92.57 121.0488
Weighted KNN 89.14 382.2346
86.97 292.4334
92.52 274.2535
90.3 189.3324
93.28 146.7282
Ensemble baggage tree 88.5 402.3328
85.71 309.4543
90.5 236.3453
89.83 190.2324
92.89 151.3453
MCSVM 91.98 220.2332
90.54 197.4324
94.24 152.9864
93.9 138.3332
97.2 111.1375
The best results are given in bold.

Kvasir-v2 Dataset Results: Table 3 shows the outcomes of the proposed framework on the Kvasir-v3 dataset. This classification performance of the selected classifiers is computed for all the middle steps. The fine tree classifier obtained the highest accuracy of 92.57% for the fusion step. The weighted KNN and ensemble tree classifiers also obtained the best accuracy of 93.28% and 91.98%, respectively, for the D-CCA-based fusion step. For the D-CCA based fusion process, the MCSVM classifier had a higher accuracy of 97.20% that is improved than the rest of the selected classifiers. Fig. 10 also illustrates the confusion matrix of MCSVM. For this dataset, the shortest execution time was 111.1375 seconds for MCSVM, which is also plotted in Fig. 11. According to the results, the accuracy of the proposed framework jumped and computational time decreased after the optimization step. However, the D-CCA fusion step gave a better performance based on both accuracy and time.

Fig. 10. Confusion matrix of the proposed framework for Kvasir-v2 dataset.


Fig. 11. Comparison of middle steps of the proposed framework based on computational time on Kvasir-v2 dataset.


Discussion
The detailed analysis of the proposed framework is conducted in this section. The results of each selected dataset are given in Tables 1–3. MCSVM performed better for all three datasets. The confusion matrixes of MCSVM are illustrated in Figs. 6, 8, and 10. The testing times are also plotted in Figs. 7, 9, and 11 for each dataset, respectively, showing the shortest time consumed by the D-CCA fusion step. As shown in Fig. 2, the proposed framework consists of several important steps. The contrast enhancement step effects the classification accuracy of the entire framework. Therefore, we conducted a comparison based on the following two steps comprised of (1) importance of contrast enhancement in the proposed framework based on the accuracy value and (2) difference in the computational time without employing the contrast enhancement step in the proposed framework. Tables 4–6 show the effect of the contrast enhancement step in the proposed classification framework of GI diseases. The values given in these tables show that accuracy of the proposed framework is improved after employing the contrast enhancement technique; however, without contrast enhancement, accuracy is degraded by an average of 6%. On the other hand, the computational time is minimized without employing the contrast enhancement step. Based on this observation, it is clear that the addition of some important steps (i.e., enhancement) increases the accuracy of the system, provided that the system’s computational time is affected as well.

Table 4. Comparison of our classification results with or without contrast enhancement step on the CUI WCE dataset
Classifier Feature Performance measure
MobileNet NasNet Mobile HMFO-MobileNet HMFO- NasNet Mob Fusion Accuracy (%) Time (s)
Contrast enhanced dataset 94.65 222.8767
92.87 212.324
97.64 122.8343
96.79 108.3323
99.42 91.0131
Without contrast enhanced dataset 88.62 201.8579
83.55 191.9343
89.88 102.8553
85.56 89.0684
92.28 73.9044
The best results are given in bold.

Table 5. Comparison of our classification results with or without contrast enhancement step on Kvasir-v1 dataset
Classifier Feature Performance measure
MobileNet NasNet Mobile HMFO-MobileNet HMFO- NasNet Mob Fusion Accuracy (%) Time (s)
Contrast enhanced dataset 91.98 176.7832
90.54 157.8594
94.24 102.3899
93.9 88.3243
97.85 71.0315
Without contrast enhanced dataset 86.73 150.4759
81.87 131.0352
88.2 92.805
84.07 73.6894
91.69 61.1135
The best results are given in bold.

Table 6. Comparison of our classification results with or without contrast enhancement step on Kvasir-v2 dataset
Classifier Feature Performance measure
MobileNet NasNet Mobile HMFO-MobileNet HMFO- NasNet Mob Fusion Accuracy (%) Time (s)
Contrast enhanced dataset 91.98 220.2332
90.54 197.4324
94.24 152.9864
93.9 138.3332
97.2 111.1375
Without contrast enhanced dataset 85.67 192.9044
79.99 170.2432
86.01 122.0352
83.78 112.5065
92.96 98.6849
The best results are given in bold.

At the end, the proposed framework’s accuracy is compared with state-of-the-art (SOTA) techniques, as given in Table 7 [21, 34, 4247]. In [21], authors used the Kvasir-v2 dataset and achieved an accuracy of 96.33%. Later on, authors in [42] obtained an accuracy of 96.33% on the Kvasir-v2 dataset. The proposed framework obtained an improved accuracy of 97.2% on the Kvasir-v2 dataset. In [43], authors used the Kvasir-v1 dataset and attained an accuracy of 94.46% that was further improved in [44] up to 97%. The proposed framework achieved an improved accuracy of 97.85% on the Kvasir-v1 dataset. Researchers in [34] used the CUI Wah WCE dataset and attained an accuracy of 96.50% that was further improved in [45] up to 98.40%. The proposed framework achieved an accuracy of 99.42% which is better than any SOTA method. Overall, the proposed framework showed an improved accuracy, dominating any SOTA method.

Table 7. Comparison of our results with SOTA techniques
Study Year Dataset Accuracy (%)
Pogorelov et al. [21] 2017 Kvasir-v2 94.2
Gamage et al. [46] 2019 Kvasir-v2 90.74
Majid et al. [34] 2020 CUI Wah WCE 96.5
Khan et al. [45] 2020 CUI Wah WCE 98.4
Kumar et al. [43] 2021 Kvasir-v1 94.46
Yogapriya et al. [42] 2021 Kvasir-v2 96.33
Al-Adhaileh et al. [44] 2021 Kvasir-v1 97
Ahmed [47] 2022 Kvasir-v1 90.17
Proposed CUI Wah WCE 99.42
Kvasir-v1 97.85
Kvasir-v2 97.2


Conclusion

A deep learning and moth-crow modified optimization algorithm-based frameworks are proposed for GI disease classification. The contrast enhancement-based data augmentation step is employed for better training of the fine-tuned models. Features are extracted from global average pooling layers that are optimized through the moth-crow modified optimization algorithm. The best selected features are fused using the D-CCA-based fusion technique. The final fused vector is then passed on to machine learning classifiers for classification. The experimental process is conducted on three datasets of CUI Wah WCE, Kvasir-v1, and Kvasir-v2, resulting in improved accuracy through our framework. Based on the results, we arrived at the following conclusions:

The proposed contrast enhancement technique shows an improvement in the classification accuracy.

The learning of fine-tuned models from the middle layers takes some time during the training process but do return better results later.

The proposed moth-crow modified optimization algorithm improves the classification accuracy and reduces the testing time due to a fewer number of predictors.

The D-CCA fusion method removes the recurrent features and improves the overall accuracy.

The limitation of this work is the addition of a contrast enhancement step that increases the computational time. In the future, the “EfficientNet” pre-trained model will be considered for feature extraction. Furthermore, the issue of irrelevant and redundant features will be resolved by employing a search point algorithm based on the Newton Raphson. Moreover, our framework will be fine-tuned to work smoothly with the Internet of medical things and blockchain-assisted systems [48, 49].


Author’s Contributions

Conceptualization, MAK, KM, SHW. Methodology, MAK, KM, OT, SHW. Software, MAK, KM, AM, OT, SHW. Validation, SA, AB, AA. Formal analysis, SA, AB, AA.Investigation, SA, AB, AA. Resources, SA, AB, AA.Data curation, SA, AB, OT, AA. Writing—original draft preparation, MAK, KM, SHW. Writing—review and editing, AM, OT, SHW. Visualization, AM, OT.Supervision, KM, AM.Project administration, AM,MAK.Funding acquisition, SHW, AM, OT. All authors have read and agreed to the published version of the manuscript.


Funding

This research work was partially supported by Chiang Mai University and HITEC University.


Competing Interests

The authors declare that they have no competing interests.


Author Biography

Author
Name : Muhammad Attique Khan
Affiliation : HITEC University Taxila
Biography : Muhammad Attique Khan (Member IEEE) earned his Master and Ph.D degree in Human Activity Recognition for Application of Video Surveillance and Skin Lesion Classification using Deep Learning from COMSATS University Islamabad, Pakistan. He is currently Lecturer of Computer Science Department in HITEC University Taxila, Pakistan. His primary research focus in recent years is medical imaging, COVID19, MRI analysis, Video Surveillance, Human Gait Recognition, and Agriculture Plants. He has above 180 publications that have more than 4420 citations and impact factor 500+ with h-index 43. He is reviewer of several reputed journals such as IEEE transaction on Industrial Informatics, IEEE transaction of Neural Networks, Pattern Recognition Letters, Multimedia Tools and Application, Computers and Electronics in Agriculture, IET Image Processing, Biomedical Signal processing Control, IET Computer Vision, Eurasipe Journal of Image and Video Processing, IEEE Access, MDPI Sensors, MDPI Electronics, MDPI Applied Sciences, MDPI Diagnostics, and MDPI Cancers.

Author
Name : Khan Muhammad
Affiliation : Sungkyunkwan University
Biography : Khan Muhammad (Member, IEEE) received the Ph.D. degree in digital contents from Sejong University, Seoul, South Korea, in 2019. He is currently an Assistant Professor with the School of Convergence, College of Computing and Informatics, Sungkyunkwan University, Seoul. He is currently a Professional Reviewer for over 100 well-reputed journals and conferences. He has registered eight patents and published over 170 articles in peer-reviewed international journals and conferences in his research areas. His research interests include medical image analysis (brain magnetic resonance imaging, diagnostic hysteroscopy, and wireless capsule endoscopy), information security (steganography, encryption, watermarking, and image hashing), video summarization, computer vision, fire/smoke scene analysis, and video surveillance.

Author
Name : ShuiHua Wang
Affiliation : University of Leicester
Biography : Dr. Shuihua Wang received her Ph.D. degree from Nanjing University in 2017. She worked as Assistant Professor at Nanjing Normal University (2013-2018) and served as a Research Associate at Loughborough University (2018-2019) and at the University of Leicester (2019-2021). Now, she is working as Lecturer at the University of Leicester (2021-now). Her research interests focus on Machine learning, Deep learning, Image processing, Information fusion, Data analysis. She has published high-quality papers in peer-reviewed international journals and conferences in these research areas. So far, she has successfully secured several external funding. She is rewarded as 2019 Highly Cited Researcher by Clarivate and 2020 Highly Cited Chinese Researcher by Elsevier.

Author
Name : ShtwaiAlsubai
Affiliation : Prince Sattam bin Abdulaziz University
Biography : ShtwaiAlsubai is an assistant professor in Computer Science at the Prince Sattam Bin AbdulAziz University. He received the bachelor degree in information system from King Saud University, KSA, in 2008, the master degree in computer science from CLU, USA, in 2011, and the PhD degree from the university of Sheffield, UK, in 2018. His research interests include XML, XML query processing, XML query optimization, machine learning and natural language processing.

Author
Name : Adel Binbusayyis
Affiliation : Prince Sattam bin Abdulaziz University
Biography : ADEL BINBUSAYYIS is currently an Assistant Professor in Computer Science at Prince Sattam Bin Abdulaziz University. He received his PhD degree from the University of Manchester, UK in 2016. He is working as the dean of the college of computer engineering and sciences at Prince Sattam bin Abdulaziz University. His research interests include AI security, Applied Cryptography, Access control, and big data analysis and processing.

Author
Name : Abdullah Alqahtani
Affiliation : Prince Sattam bin Abdulaziz University
Biography : Abdullah Alqahtani is an assistant professor in Computer Science at the Prince Sattam Bin AbdulAziz University. He received the bachelor degree in computer science from King Saud University, KSA, in 2007, the master degree in advanced computer science from university of Leicester, UK, in 2011, and the PhD degree from the university of Leicester, UK, in 2020. His research interests include model-driven development, big data processing and analytics, graph transformation theory and its applications in machine learning and AI.

Author
Name : Arnab Majumdar
Affiliation : Imperial College London
Biography : Prof. Dr. Arnab Majumdarlis the Professor at the Imperial College London. He is also the DeputyDirector(External Partnerships) of the ESRC London Interdisciplinary Social Science Doctoral Training Partnership (LISS DTP), involving the Universities of Kings College London, QueenMary and Westfield and Imperial College London.His PhD was completed in 2003 at ImperialCollege on the Estimation of Airspace Capacity in Europe. He also has MSc. degrees in Transportfrom Imperial College and Cognitive Neuropsychology from University College London.In conducting his research, he has worked closely with a number of organisations both nationally, e.g. Public Health England, Abellio, easyJet, and internationally, e.g.

Author
Name : OrawitThinnukool
Affiliation : Chiang Mai University
Biography : Dr. OrawitThinnukoolreceived the Ph.D. degree in research methodology and data analytics from the Prince of Songkla University, He is currently the Head of the Research Excellence Center Unit. He is also working with the Department of Modern Management, College of Arts, Media and Technology, Chiang Mai University. He has more than seven years of teaching, faculty administrative, and research experience include computational science, educational technology, and informatics. He has published more than 40 research articles and received a scholarship from Thai Government and Thai funding agencies.


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Muhammad Attique Khan1, Khan Muhammad2, Shui-Hua Wang3, Shtwai Alsubai4, Adel Binbusayyis4, Abdullah Alqahtani4, Arnab Majumdar5, Orawit Thinnukool6,*, Gastrointestinal Diseases Recognition: A Framework of Deep Neural Network and Improved Moth-Crow Optimization with DCCA Fusion, Article number: 12:25 (2022) Cite this article 1 Accesses

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  • Recived24 January 2022
  • Accepted26 February 2022
  • Published30 May 2022
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