10.3934 Era.2023383
10.3934 Era.2023383
10.3934 Era.2023383
DOI: 10.3934/era.2023383
El
ect
ronic Received: 16 October 2023
Re
searchArc
hiv
e Revised: 08 November 2023
Accepted: 09 November 2023
http://www.aimspress.com/journal/era Published: 04 December 2023
Research article
Chengtian Ouyang1 , Huichuang Wu1 , Jiaying Shen2 , Yangyang Zheng2 , Rui Li2 , Yilin Yao3 and
Lin Zhang3, *
1
School of Information engineering, Jiangxi University of Science and Technology, Ganzhou
341000, China
2
School of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China
3
College of Software Engineering, Jiangxi University of Science and Technology, Nanchang
330013, China
Abstract: The emergence of COVID-19 has broken the silence of humanity and people are gradually
becoming concerned about pneumonia-related diseases; thus, improving the recognition rate of
pneumonia-related diseases is an important task. Neural networks have a remarkable effectiveness
in medical diagnoses, though the internal parameters need to be set in accordance to different data
sets; therefore, an important challenge is how to further improve the efficiency of neural network
models. In this paper, we proposed a learning exponential distribution optimizer based on chaotic
evolution, and we optimized Resnet50 for COVID classification, in which the model is abbreviated
as IEDO-net. The algorithm introduces a criterion for judging the distance of the signal-to-noise
ratio, a chaotic evolution mechanism is designed according to this criterion to effectively improve
the search efficiency of the algorithm, and a rotating flight mechanism is introduced to improve the
search capability of the algorithm. In the computed tomography (CT) image data of COVID-19, the
accuracy, sensitivity, specificity, precision, and F1 score of the optimized Resnet50 were 94.42%,
93.40%, 94.92%, 94.29% and 93.84%, respectively. The proposed network model is compared with
other algorithms and models, and ablation experiments and convergence and statistical analyses are
performed. The results show that the diagnostic performance of IEDO-net is competitive, which
validates the feasibility and effectiveness of the proposed network.
1. Introduction
As the quality of human life improves, so do the diseases that need to be diagnosed using current
technologies through early detection and treatment to reduce pain and suffering [1,2]. The most recent
outbreak of COVID-19 has led to a worldwide crisis at various levels, which has not yet been
eradicated; moreover other pneumonia-related diseases are attacking us, making it important to
further improve the diagnosis of these diseases [3].
In recent years, scholars have proposed many methods to diagnose and identify diseases, and deep
learning models are a powerful and beneficial part of effective disease diagnoses [4–6]. For example,
Hussain et al. proposed a novel convolutional neural network (CNN) network of CoroDet to perform
the diagnoses of multiple classifications from X-ray and CT scan images of the chest [7]. Ozdemir
generated six-axis mapped images from electrocardiograph (ECG) data a via grey scale co-occurrence
matrix (GLCM) and imported them into a new CNN for the diagnosis of COVID-19 [8]. Ismael fused
multiple deep CNN models and multiple kernel functions of support vector machine (SVM)
classifiers trained in an end-to-end manner to diagnose a dataset of X-ray chest images [9]. Kc et al.
performed diagnostic experiments on eight pre-trained models for COVID-19 and introduced
migration learning techniques [10]. Muhammad and Hossain proposed a new CNN model with fewer
parameters and properties [11]. Wang et al. developed a weakly-supervised deep learning framework
that could accurately predict the probability of infection and lesion areas in patients [12]. However,
it has the problem that different hyperparameters need to be set for different data sets or
experimental environments in order to maximise the benefits of the model itself, so the setting of
hyperparameters is an important challenge. The metaheuristic algorithm is widely used for the
optimization effect of depth models, and it has the advantage of improved robustness and global
optimization capability [13]. In the past two decades, research on metaheuristics has been gradually
advanced [14,15], and scholars have proposed classical algorithms such as particle swarm
optimization (PSO) [16], differential evolution (DE) [17], ant colony optimization (ACO) [18], etc.
Some of the more popular algorithms in recent years are grey wolf optimizer (GWO) [19], whale
optimization algorithm (WOA) [20], sparrow search algorithm (SSA) [21], harris hawks optimization
(HHO) [22], manta ray foraging optimization (MRFO) [23], exponential distribution optimizer
(EDO) [24], etc. All these algorithms have been better used in the field of COVID-19 classification
and diagnoses. For example, Dixit et al. incorporated DE and PSOs for optimal feature extraction,
and fed the optimized features to an SVM to obtain an improved accuracy [25]. Júnior et al. used
multiple CNN models to extract depth features and used PSO optimized eXtreme Gradient Boosting
(XGBoost) for classification [26], which included feature extraction using mel frequency cepstral
coefficients (MFCCs) and classification using an PSO optimized learning machine (ELM) [27].
Elaziz et al. fused DE with MRFO to optimize the k-nearest neighbor (KNN) classifier and obtained
better results [28]. El-Kenawy et al. used an improved advanced squirrel search algorithm (ASSOA)
to optimize the multilayer perceptron and achieved certain classification results [29]. Pathan et al.
combined the whale algorithm with the Bat algorithm (BAT) to optimize the CNN and performed
classification by ADaBoost [30]. Basu et al. proposed a local search based and acoustic search
algorithm for feature extraction of COVID-19 and used multiple deep convolutional networks for
classification [31]. Nadimi-Shahraki et al. proposed a binary enhanced WOA for COVID-19 feature
extraction to obtain an improved recognition accuracy [32]. Elghamrawy and Hassanien proposed a
diagnostic and prediction model optimized by a WOA [33]. Goel et al. optimized a CNN with a
GWO to have an improved classification accuracy of chest X-ray images [34]. Hu et al. fused a CNN
with an extreme learning machine (ELM) and used the Chimp optimization algorithm to improve the
reliability of the net training [35]. Singh et al. proposed a multi-objective differential evolution
algorithm to tune the CNN to find the maximum classification accuracy [36]. Iraji et al. used a
binary differential evolution algorithm to extract features and classify them by SVM [37]. Sahan et al.
used an artificial bee colony algorithm to extract features and classify them with a multilayer
perceptron [38]. Sadeghi et al. proposed a novel deep learning framework with a novel multi-habitat
migrating artificial bee colony (MHMABC) algorithm for optimized training [39]. Balaha et al.
proposed a HHO to optimize multiple pre-trained networks and improve the classification accuracy of
COVID-19 [40]. Bahgat et al. optimized 12 architectures of MRFO for CNN to improve the
classification performance [41]. Currently, more and more optimized models are proposed, though the
design of the algorithm may not take into account the reliability of the iterative optimization of the
algorithm, as the time and resources spent in the optimization process are huge; on the other hand, the
learning ability of the network model is based on a large number of samples trained, thus improving
the learning ability of finite samples is an important task. We need to find a reliable solution at
each iteration that the efficiency of the model can improve the efficiency of the model and the work
can be meaningful.
Based on the aforementioned analyses, while considering the reliability and learning at each
iteration, this paper proposes a learning exponential distribution optimizer with a chaotic evolutionary
mechanism, introduces a signal-to-noise distance to judge the distance of individuals, selects a
suitable chaotic evolutionary mechanism by distance, and introduces a rotating flight strategy to
enhance the local search capability of the algorithm. The hyperparameters of the optimized Resnet50
network model were put into practice and diagnosed in COVID-19 images. The experimental results
show that the classification accuracy using the optimized Resnet50 network model is high. The
specific work and innovations are as follows:
• a balanced individual selection method based on signal-to-noise distance is proposed;
• a chaos-based evolutionary mechanism is proposed;
• an IEDO algorithm and optimize the hyperparameters of Resnet50 in proposed;
• the model is compared with a variety of other algorithms and models in the COVID-19 dataset.
The overall structure of this paper is as follows: Section 2 describes Resnet50 and the original
EDO algorithm; Section 3 describes and analyses the proposed algorithm; Section 4 describes the
entire experimental procedure and methodology; Section 5 performs the COVID-19 classification
experiments and analysis; and the last section concludes the paper and presents future work.
2. Related theory
2.1. Resnet50
ResNet is a deep CNN model that is used as the backbone of the model due to its excellent
performance [42]. Resnet50 is a 50-layer version of ResNet, in which the residual connection and
residual block structures are implemented, with each residual block containing two convolutional
layers and one residual connection. By implementing the aforementioned structures, Resnet50 greatly
alleviates the problem of gradient disappearance and gradient explosion during training, and has a
high accuracy and generalisation capability [43].
The first 49 layers of the Resnet50 network are convolutional and the final layer is a fully
connected layer, whose structure can be divided into seven parts. As shown in Figure 1, Stage 0
performs the computation of convolution, regularization, activation function and maximum pooling
without residual blocks; Stage 1 through 4 contain residual blocks. At the beginning, an image of
size 224 × 224 × 3 represents the input; after the first five convolution layers, the output is a feature
map of size 7 × 7 × 2048. After a utilizing pooling layer to convert the features into feature vectors,
the classifier calculates and outputs the category probabilities. Resnet50 has a complex structure, and
the internal hyperparameters also affect the learning efficiency between layers when facing different
datasets. An important work of this paper is to further improve the classification recognition rate
of Resnet50.
a. Generate N sets of solutions using a random distribution technique, with D values in each set of
solutions, and set the corresponding maximum number of iterations (termination condition).
b. Start the iteration by constructing a memoryless zero matrix to simulate the memoryless nature
of the algorithm, with a size equal to the overall initial generation.
c. During the development phase, the memoryless matrix is used to simulate the memoryless
properties in order to preserve the previously generated solutions, regardless of their history,
which will become important members for updating the new solutions. As a result, these
solutions were divided into two categories: winners and losers. In addition, some features of the
exponential distribution are used, such as mean, exponential rate and variance, are used. The
winners will move in the direction of the bootstrapped solution, while the losers will move in the
direction of the winner, with the aim of finding a global optimum around it.
d. In the exploration phase, the new solution uses two winners chosen at random from the original
population and updates the mean solution. Initially, both the mean solution and the variance are
far from the global optimum. The distance between the mean solution and the global optimum
gradually decreases until a minimum is reached through the optimization process.
e. A switch parameter is used to determine whether to perform the exploration phase or the
exploitation phase, where a is a uniform random number between [0,1]. If a < 0.5, then the
exploitation is carried out as follows:
r1 · (mlit − σ2 ) + r2 · Xguide
t t
= mlit
(
if Xwin,i
Xit+1 = (2.1)
r2 · (mlit − σ2 ) + log(∅) · Xwin,i
t
otherwise
Xit+1 = Xwin,i
t
− M t + (r3 · Z1 + (1 − r − 3) · Z2 ) (2.2)
t
Xwin,best1 + Xwin,best2
t
+ Xwin,best3
t
t
Xguide = (2.3)
3
1−t
r3 = d × q; d = (2.4)
M
Z1 = M − D1 + D2 ; Z2 = M − D2 + D1 (2.5)
f. In the above equation, r1 = (q)10 , r2 = (q)5 , q is a uniform random number between [-1,1]. ∅ is a
uniform random number between [0,1]. M represents the mean value of the population, and mlit is
t t t
the i-th winner at the current stage. Xguide is the guiding solution at t iterations. Xwin,best1 , Xwin,best2 ,
t
Xwin,best3 are the top three best solutions in the matrix. Xwin,RD1 and Xwin,RD2 denote randomly
selected individuals within the population.
g. After generating the new solutions, check the boundaries of each solution. Then, the solutions are
saved in a memoryless matrix.
h. Use greedy mechanisms to update new solutions from the development and exploration phases of
the original population. If the new solution is good, it will be updated in the original population.
i. If the termination condition is reached, the algorithm ends and the final optimal solution and
position are outputs; otherwise, it returns to the second step.
this paper introduces the SNR distance to measure different situations between different data, and the
SNR distance between data and is defined as follows:
v(h j − hi ) v(ni j )
ds(pi , p j ) = = (2.7)
v(hi ) v(hi )
Pn
(x −µ)2 )
where v(x) = i=1 ni denotes the variance of x, µ denotes the mean of x and n denotes the
dimensionality of x. Longer SNR distances indicate larger differences between the anchor and the
comparison data; therefore, the signal-to-noise ratio distance measure can be used as a similarity
measure instead of the Euclidean distance measure in metric learning.
v(xi − xbest )
DS i = (3.1)
v(xbest )
i denotes the i-th individual in the population, thus resulting in a DS that is an N*1 matrix. Then, the
DS and F are normalized and multiplied separately to obtain an evaluation score:
The purpose of normalization is to normalize the SNR distances to a certain range, which improves
the reliability of the balanced individuals by eliminating the influence of the anomalous positions on
the positions of other individuals to some extent. Additionally, the obtained score is an N*1 matrix,
where the individual with the highest score value is designated as the balanced individual BI.
logistic represents a logistic sequence of 1 × dim, where the logistic sequence generation
schematic is shown in Figure 2. The logistic sequence is able to produce a more uniform
distribution of values, which makes the algorithm search more comprehensive and enables it to
balance the global exploration of the algorithm with local exploitation to better explore solutions
compared to CX. Xm represents the average position of the population.
b. If f (LX) > f (CX) > f (Lbest), then it means that a solution better than Lbest is not found twice in
a row, but the quality of the position in CX is higher than that of LX, and a local search between
CX and Lbest needs to be considered.
Tent denotes a tent sequence of 1 × dim, and the tent sequence generation schematic is shown
in Figure 3. Most of the distribution produced by the tent sequence is concentrated above 0.5.
The current population is not able to find a better solution, and it needs to improve a certain
global exploration ability to effectively improve the situation; therefore, the tent sequence can
better meet this need, which can gradually make CX close to Lbest, and further get rid of the
current dilemma.
c. If f (Lbest) < f (LX) < f (CX), then it indicates that the interval between two searches is invalid,
and the quality of the current population is worse than that of the previous generation, thus
indicating that the population may fall into a local extreme state. It is necessary to consider
improving the global search ability of the population and finding a reasonable optimal position.
The specific formula is as follows:
Gaussian represents a gaussian sequence of 1 × dim, and the gaussian sequence generation
schematic is shown in Figure 4. BI is the balanced individual produced earlier, thus causing the
population to converge to different positions and improving the search ability through the
difference between Lbest and the current individual. The gaussian sequence produces slightly
more values below 0.5, which allows the current individual to approach the optimal solution
faster and improve the accuracy of the solution.
Taking the aforementioned analyses into account, the generation of three chaotic sequences is
able to produce different search methods, which sufficiently improves the accuracy of the search.
In order to verify that this mechanism can improve the search efficiency of the algorithm, this
section compares the original EDO algorithm with EDO (C+EDO) with the addition of the
chaotic evolution mechanism using the Sphere function for the experiment; the number of
population iterations are both at 50, and the optimal objective function values obtained from
each of them are presented in Figure 5. Figure 5 shows that the search of the C+EDO algorithm
at each iteration is better than the previous one, or at least is not worse than the previous
solution; EDO appears to be an invalid search, though it is impossible to avoid the occurrence of
invalid search, and the least possible while improving the search efficiency of the algorithm.
Through the aforementioned analysis, the feasibility of the chaotic evolution mechanism can be
verified, so as to improve the efficiency of the iterative search of the algorithm. It is worth noting
that the aforementioned judgement conditions do not require an additional number of
calculations, as they are automatically obtained during the algorithm optimization process.
algorithm based on chaotic evolution, which combines the balanced individual of signal-to-noise
distance selection with the chaotic evolution mechanism, and finally introduces a RFS, so as to
further improve the global and local search capability of the algorithm. The specific pseudo-code is
as follows:
Algorithm 1 Algorithm 1 IEDO
Input: Population size (N), dimensionality of the problem (d), and upper and lower bounds.
Output: Optimal solution position Xtwin,best and optimal solution fmin
1: Random initialization to obtain a Xtwin matrix
2: Find the optimal solution fmin and the optimal location of the optimal solution Xtwin according to
the fitness function
3: t = 1
4: Generate a memoryless matrix ml = Xwin
5: while (t ≤ T ) do
6: rank the populations according to their fitness values to obtain the top three best individuals
7: Calculate Eq (2.3)
8: Balancing the selection of individuals
9: Generate sequences of logistic, tent, gaussian, respectively based on dimensions
10: if f (x) < f (Lbest) then
11: update the Eq (3.3) % logistic sequence
12: else if f (Lx) > f (Cx) > f (Lbest) then
13: update the Eq (3.4) % tent sequence
14: else
15: update the Eq (3.5) % gaussian sequence
16: end if
17: for i = 1:N do
18: if α < 0.5 then
19: update the Eq (2.1)
20: else
21: update the Eq (2.2)
22: end if
23: end for
24: for i = 1:N do
25: update the Eq (3.6) % RFS
26: end for
27: Calculate the fitness values within the population and obtain the optimal solution fmin and
t
Xwin,best
28: t=t+1
29: end while
Return Outputs
4. Methods
Resnet50 is one of the DL models with a unique feature extraction for input images during image
classification [49]. The extraction of features in Resnet50 is preformed by several convolutional and
pooling layers. A fully connected softmax layer performs the classification. The setting of
hyperparameters affects the training capability of the network; therefore, IEDO needs to be used to
more rationally optimize these parameters to maximize the classification capability of the network as
much as possible. The hyperparameters to be optimized in this paper include Momentum,
InitialLearnRate, MaxEpochs, and ValidationFrequency, which are first optimized using IEDO and
then trained and tuned using a learning gradient with momentum (SGDM) algorithm. The flow chart
of the hyperparameters using the IEDO algorithm Resnet50 is shown in Figure 6. The general process
is described as follows:
b. Use the classification error rate of the model as the objective function, keeping the identification
labels corresponding to the minimum error rate.
c. Import the above solution into the SGDM; then train and optimize through it to obtain the
classification results.
5. Experiments
From Table 1, the average value and the worst value of IEDO are better, which verifies that IEDO
has a certain stability and competitiveness; on the other hand, the P-value of the two algorithms
is 0.01%, which indicates that there is a certain difference in the performance of the two algorithms.
Through the statistical results, it can be seen that EDO has a clear advantage in the value, which
proves that the improvement of IEDO is somewhat effective.
continually improved versions proposed that are valuable to compare with these models.The
classification result table for each model is shown in Table 2.
Table 2. Classification result table for each model.
Algorithms Inception Vgg16 Vgg19 Alexnet Resnet IEDO-net
Accuracy 73.21% 53.13% 53.13% 67.41% 87.95% 94.42%
From Table 2, we can see that the recognition accuracy of IEDO-net is the highest, with a
recognition rate of 94.42%. The recognition accuracy of Resnet without optimization is 87.95%, and
the recognition accuracy of other models is lower; thus, it can be seen that it is more meaningful to
optimize Resnet.
It can be seen from Figure 5 and Table 3 that IEDO and the other basic algorithms for pneumonia
diagnoses are tested under the same circumstances, and the algorithm is ranked from the highest
accuracy to the lowest accuracy. It can be concluded that the accuracy of the DE algorithm in the
diagnoses of pneumonia was 83.93%, the number of correct diagnoses of normal was 102, and the
number of correct diagnoses of COVID was 86. The accuracy of the PSO algorithm in the diagnoses
of pneumonia was 86.61%, the number of correct diagnoses of normal was 109, and the number of
correct diagnoses of COVID was 85. The accuracy of the MRFO algorithm in diagnoses of
pneumonia was 87.95%, the number of correct diagnoses of normal was 105, and the number of
correct diagnoses of COVID was 92. The accuracy of the EDO algorithm in the diagnoses of
pneumonia was 89.73%, the number of correct diagnoses of normal was 103, and the number of
correct diagnoses of COVID was 98. The accuracy of the GWO algorithm in the diagnoses of
pneumonia was 89.73%, the number of correct diagnoses of normal was 110, and the number of
correct diagnoses of COVID was 91. The accuracy of the WOA algorithm in the diagnoses of
pneumonia was 91.07%, the number of correct diagnoses of normal was 111, and the number of
correct diagnoses of COVID was 93. The accuracy of the ASSOA [24] algorithm in the diagnoses of
pneumonia was 91.07%, the number of correct diagnoses as normal was 111, and the number of
correct diagnoses as COVID was 93. The accuracy of the WOABAT [25] algorithm in the diagnoses
of pneumonia was 90.18%, the number of correct diagnoses as normal was 111, and the number of
correct diagnoses as COVID was 91. The accuracy of the DEPSO [20] algorithm in the diagnoses of
pneumonia was 91.52%, the number of correct diagnoses as normal was 108, and the number of
correct diagnoses of COVID was 97. The accuracy of the IEDO algorithm in the diagnoses of
pneumonia was 94.20%, the number of correct diagnoses of normal was 112, and the number of
correct diagnoses of COVID was 96.
It can be seen that compared with the other basic algorithms used for pneumonia diagnoses, the
IEDO algorithm has the highest accuracy, surpassing all the other algorithms. The IEDO algorithm
has the largest number of correct diagnoses. It can be seen from Table 3 that IEDO performs better
than the other basic algorithms for pneumonia diagnosis in other aspects of performance, which can
well reflect the superiority of IESO algorithm.
To further verify the effectiveness of IEDO using both chaotic evolution and spiral flight, we
compared IEDO with an algorithm that does not use chaotic evolution (called EDO-1) and an
algorithm that does not use spiral flight (called EDO-2). The accuracy of the EDO-1 algorithm in the
diagnoses of pneumonia was 91.52%, the number of correct diagnoses of normal was 113, and the
number of correct diagnoses of COVID was 92. The accuracy of the EDO-2 algorithm in the
diagnosis of pneumonia was 91.96%, the number of correct diagnoses of normal was 108, and the
number of correct diagnoses of COVID was 98. Comparing the above data with the data obtained by
the IEDO algorithm, it can be concluded that the accuracy of EDO-1 and EDO-2 are lower than that
of the IEDO algorithm. Although the EDO-1 algorithm has higher sensitivity, other performances
have regressed. In the EDO-2 algorithm, all performances regressed. The simultaneous use of the two
mechanisms can improve the performance of IEDO. Although some of the performance will be
reduced, the number of performance decreases is small and not obvious, while most of the
performances are improved, which reflects the rationality and performance superiority of the IEDO
algorithm using the mixed mechanism.
The ROC curve shows that the curve of IEDO is closer to the (0, 1) position, thus indicating a better
predictive capability of the IEDO optimized network: the higher the sensitivity and the lower the false
positive rate, the better the performance of the diagnostic method.
From Table 5, we can see that the IEDO network is better able to recognise the 746 CT images in
the dataset, and purely in terms of metrics, the IEDO network ranks third in terms of its classification
performance. The best result is the Trans–CNN network, which is first in all metrics, but has a large
sample size. The next best result is MADE-DBM, which has a sample image size of 1790. The size
of the number affects the accuracy of the model, which intuitively serves to increase the number of
learning samples, but also increases the noise of the image; therefore, the strengths and weaknesses
of the model cannot be analyzed purely in terms of recognition accuracy. Taken together, the IEDO
network achieves reliable classification accuracy with a small number of samples, which is competitive
and has some value and significance in intelligent healthcare.
6. Conclusions
In order to further improve the classification recognition rate of COVID, the paper proposed an
optimized Resnet50 network model by IEDO (IEDO-net). IEDO introduces a signal-to-noise ratio
distance determination selection on the basis of EDO to select a reasonable equilibrium individual and
to reduce the probability of falling into a local optimum; then, it proposes a chaotic evolutionary
mechanism to improve the efficiency of the algorithm search; finally, it introduces a spiral flight
mechanism to improve the local search ability of the algorithm. In the CT dataset of COVID, the
IEDO-net has a high classification accuracy and is compared with other networks to verify the
feasibility of the IEDO-net, and the effectiveness of the algorithm is verified by ablation experiments.
Although IEDO-net has achieved some achievements, it has some problems. First, as we all know,
there are many kinds of diseases, and the same disease may also have different categories; the
classification experiment designed in this paper is relatively simple, while the network considered is
more general. Second, the diagnostic accuracy of the model is high, but there is still an error rate, and
it cannot completely replace the judgement of the treating professional doctor. Meanwhile, the
training data is completely dependent on the given images, and the initial labels are also subject to
human-set errors. Third, there is less privacy protection for the patients. Taken together, our next step
is to consider maximizing privacy protection based on federated learning and improving the correct
diagnosis rate of different diseases to greatly improve the reliability of smart healthcare. The main
work is divided into the following three areas:
• designing more efficient meta-heuristic optimization algorithms;
• optimizing up-to-date and rational networks for diagnosis of more classes of diseases;
• and incorporating federated learning to maximize the efficiency of network models while
protecting data privacy.
The authors declare they have not used Artificial Intelligence (AI) tools in the creation of this article.
Conflict of interest
References
2. N. Q. K. Le, Potential of deep representative learning features to interpret the sequence information
in proteomics, Proteomics, 22 (2022), 2100232. https://doi.org/10.1002/pmic.202100232
3. P. Aggarwal, N. K. Mishra, B. Fatimah, P. Singh, A. Gupta, S. D. Joshi, COVID-19 image
classification using deep learning: Advances, challenges and opportunities, Comput. Biol. Med.,
144 (2022), 105350. https://doi.org/10.1016/j.compbiomed.2022.105350
4. O. S. Albahri, A. A. Zaidan, A. S. Albahri, B. B. Zaidan, K. H. Abdulkareem, Z. T. Al-qaysi,
et al., Systematic review of artificial intelligence techniques in the detection and classification
of COVID-19 medical images in terms of evaluation and benchmarking: Taxonomy analysis,
challenges, future solutions and methodological aspects, J. Infect. Public Health, 13 (2020), 1381–
1396. https://doi.org/10.1016/j.jiph.2020.06.028
5. T. W. Cenggoro, B. Pardamean, A systematic literature review of machine learning application
in COVID-19 medical image classification, Procedia Comput. Sci., 216 (2023), 749–756.
https://doi.org/10.1016/j.procs.2022.12.192
6. Y. Hu, K. Liu, K. Ho, D. Riviello, J. Brown, A. R. Chang, et al., A simpler machine learning model
for acute kidney injury risk stratification in hospitalized patients, J. Clin. Med., 11 (2022), 5688.
https://doi.org/10.3390/jcm11195688
7. E. Hussain, M. Hasan, M. A. Rahman, I. Lee, T. Tamanna, M. Z. Parvez, CoroDet: A deep learning
based classification for COVID-19 detection using chest X-ray images, Chaos, Solitons Fractals,
142 (2021), 110495. https://doi.org/10.1016/j.chaos.2020.110495
8. M. A. Ozdemir, G. D. Ozdemir, O. Guren, Classification of COVID-19 electrocardiograms by
using hexaxial feature mapping and deep learning, BMC Med. Inf. Decis. Making, 21 (2021), 170.
https://doi.org/10.1186/s12911-021-01521-x
9. A. M. Ismael, A. Şengür, Deep learning approaches for COVID-19 detection based on chest X-ray
images, Expert Syst. Appl., 164 (2021), 114054. https://doi.org/10.1016/j.eswa.2020.114054
10. K. Kc, Z. Yin, M. Wu, Z. Wu, Evaluation of deep learning-based approaches for COVID-19
classification based on chest X-ray images, Signal, Image Video Process., 15 (2021), 959–966.
https://doi.org/10.1007/s11760-020-01820-2
11. G. Muhammad, M. S. Hossain, COVID-19 and non-COVID-19 classification using
multi-layers fusion from lung ultrasound images, Inf. Fusion, 72 (2021), 80–88.
https://doi.org/10.1016/j.inffus.2021.02.013
12. X. Wang, X. Deng, Q. Fu, Q. Zhou, J. Feng, H. Ma, et al., A weakly-supervised framework for
COVID-19 classification and lesion localization from chest CT, IEEE Trans. Med. Imaging, 39
(2020), 2615–2625. https://doi.org/10.1109/TMI.2020.2995965
13. M. Riaz, M. Bashir, I. Younas, Metaheuristics based COVID-19 detection
using medical images: A review, Comput. Biol. Med., 2022 (2022), 105344.
https://doi.org/10.1016/j.compbiomed.2022.105344
14. D. Zhu, S. Wang, C. Zhou, S. Yan, J. Xue, Human memory optimization algorithm: A memory-
inspired optimizer for global optimization problems, Expert Syst. Appl., 237 (2024), 121597.
https://doi.org/10.1016/j.eswa.2023.121597
15. D. Zhu, S. Wang, J. Shen, C. Zhou, T. Li, S. Yan, A multi-strategy particle swarm algorithm with
exponential noise and fitness-distance balance method for low-altitude penetration in secure space,
J. Comput. Sci., 74 (2023), 102149. https://doi.org/10.1016/j.jocs.2023.102149
16. J. Kennedy, R. Eberhart, Particle swarm optimization, in Proceedings of
ICNN’95-International Conference on Neural Networks, IEEE, (1995), 1942–1948.
https://doi.org/10.1109/ICNN.1995.488968
17. K. V. Price, Differential evolution, in Handbook of Optimization: From Classical to Modern
Approach, Springer, (2013), 187–214. https://doi.org/10.1007/978-3-642-30504-7 8
18. M. Dorigo, M. Birattari, T. Stutzle, Ant colony optimization, IEEE Comput. Intell. Mag., 1 (2006),
28–39. https://doi.org/10.1109/MCI.2006.329691
19. S. Mirjalili, S. M. Mirjalili, A. Lewis, Grey wolf optimizer, Adv. Eng. Software, 69 (2014), 46–61.
https://doi.org/10.1016/j.advengsoft.2013.12.007
20. S. Mirjalili, A. Lewis, The whale optimization algorithm, Adv. Eng. Software, 95 (2016), 51–67.
https://doi.org/10.1016/j.advengsoft.2016.01.008
21. J. Xue, B. Shen, A novel swarm intelligence optimization approach: Sparrow search algorithm,
Syst. Sci. Control Eng., 8 (2020), 22–34. https://doi.org/10.1080/21642583.2019.1708830
22. A. A. Heidari, S. Mirjalili, H. Faris, I. Aljarah, M. Mafarja, H. Chen, Harris hawks
optimization: Algorithm and applications, Future Gener. Comput. Syst., 97 (2019), 849–872.
https://doi.org/10.1016/j.future.2019.02.028
23. W. Zhao, Z. Zhang, L. Wang, Manta ray foraging optimization: An effective bio-
inspired optimizer for engineering applications, Eng. Appl. Artif. Intell., 87 (2020), 103300.
https://doi.org/10.1016/j.engappai.2019.103300
24. M. Abdel-Basset, D. El-Shahat, M. Jameel, M. Abouhawwash, Exponential distribution optimizer
(EDO): A novel math-inspired algorithm for global optimization and engineering problems, Artif.
Intell. Rev., 56 (2023), 9329–9400. https://doi.org/10.1007/s10462-023-10403-9
25. A. Dixit, A. Mani, R. Bansal, CoV2-Detect-Net: Design of COVID-19 prediction model based
on hybrid DE-PSO with SVM using chest X-ray images, Inf. Sci., 571 (2021), 676–692.
https://doi.org/10.1016/j.ins.2021.03.062
26. D. A. D. Júnior, L. B. da Cruz, J. O. B. Diniz, G. L. F. da Silva, G. B. Junior, A. C. Silva,
et al., Automatic method for classifying COVID-19 patients based on chest X-ray images,
using deep features and PSO-optimized XGBoost, Expert Syst. Appl., 183 (2021), 115452.
https://doi.org/10.1016/j.eswa.2021.115452
27. M. A. A. Albadr, S. Tiun, M. Ayob, F. T. AL-Dhief, Particle swarm optimization-based
extreme learning machine for COVID-19 detection, Cognit. Comput., 2022 (2022), 1–16.
https://doi.org/10.1007/s12559-022-10063-x
28. M. A. Elaziz, K. M. Hosny, A. Salah, M. M. Darwish, S. Lu, A. T. Sahlol, New machine
learning method for image-based diagnosis of COVID-19, PLoS One, 15 (2020), e0235187.
https://doi.org/10.1371/journal.pone.0235187
42. K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in Proceedings of
the IEEE Conference on Computer Vision and Pattern Recognition, IEEE, (2016), 770–778.
43. L. Wen, X. Li, L. Gao, A transfer convolutional neural network for fault diagnosis based on
ResNet-50, Neural Comput. Appl., 32 (2020), 6111–6124. https://doi.org/10.1007/s00521-019-
04097-w
44. J. Yang, J. Yu, C. Huang, Adaptive multistrategy ensemble particle swarm optimization
with Signal-to-Noise ratio distance metric, Inf. Sci., 612 (2022), 1066–1094.
https://doi.org/10.1016/j.ins.2022.07.165
45. D. Zhu, Z. Huang, S. Liao, C. Zhou, S. Yan, G. Chen, Improved bare bones particle
swarm optimization for DNA sequence design, IEEE Trans. Nanobiosci., 22 (2022), 603–613.
https://doi.org/10.1109/TNB.2022.3220795
46. H. T. Kahraman, S. Aras, E. Gedikli, Fitness-distance balance (FDB): A new selection
method for meta-heuristic search algorithms, Knowledge-Based Syst., 190 (2020), 105169.
https://doi.org/10.1016/j.knosys.2019.105169
47. R. Zheng, A. G. Hussien, R. Qaddoura, H. Jia, L. Abualigah, S. Wang, A multi-strategy enhanced
African vultures optimization algorithm for global optimization problems, J. Comput. Des. Eng.,
10 (2023), 329–356. https://doi.org/10.1093/jcde/qwac135
48. D. Zhu, S. Wang, C. Zhou, S. Yan, Manta ray foraging optimization based on mechanics game and
progressive learning for multiple optimization problems, Appl. Soft Comput., 145 (2023), 110561.
https://doi.org/10.1016/j.asoc.2023.110561
49. R. Murugan, T. Goel, S. Mirjalili, D. K. Chakrabartty, WOANet: Whale optimized deep neural
network for the classification of COVID-19 from radiography images, Biocybern. Biomed. Eng.,
41 (2021), 1702–1718. https://doi.org/10.1016/j.bbe.2021.10.004
50. C. Szegedy, W. Liu, Y. Jia, Going deeper with convolutions, in Proceedings of the IEEE
Conference on Computer Vision and Pattern Recognition, IEEE, (2015), 1–9.
51. K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition,
preprint, arXiv:1409.1556.
52. L. Kong, J. Cheng, Classification and detection of COVID-19 X-Ray images based on
DenseNet and VGG16 feature fusion, Biomed. Signal Process. Control, 77 (2022), 103772.
https://doi.org/10.1016/j.bspc.2022.103772
53. A. Karacı, VGGCOV19-NET: Automatic detection of COVID-19 cases from X-ray images using
modified VGG19 CNN architecture and YOLO algorithm, Neural Comput. Appl., 34 (2022),
8253–8274. https://doi.org/10.1007/s00521-022-06918-x
54. A. Krizhevsky, I. Sutskever, G. E. Hinton, Imagenet classification with deep convolutional neural
networks, Commun. ACM, 60 (2017), 84–90. https://doi.org/10.1145/3065386
55. G. Muhammad, M. S. Hossain, COVID-19 and non-COVID-19 classification using
multi-layers fusion from lung ultrasound images, Inf. Fusion, 72 (2021), 80–88.
https://doi.org/10.1016/j.inffus.2021.02.013
56. I. Bankman, Handbook of Medical Image Processing and Analysis, Elsevier, 2008.
57. Y. Song, S. Zheng, L. Li, X. Zhang, X. Zhang, Z. Huang, et al., Deep learning enables accurate
diagnosis of novel coronavirus (COVID-19) with CT images, IEEE/ACM Trans. Comput. Biol.
Bioinf., 18 (2021), 2775–2780. https://doi.org/10.1109/TCBB.2021.3065361
58. Y. Pathak, P. K. Shukla, K. V. Arya, Deep bidirectional classification model for COVID-
19 disease infected patients, IEEE/ACM Trans. Comput. Biol. Bioinf., 18 (2020), 1234–1241.
https://doi.org/10.1109/TCBB.2020.3009859
59. Y. Pathak, P. K. Shukla, A. Tiwari, S. Stalin, S. Singh, P. K. Shukla, Deep transfer
learning based classification model for COVID-19 disease, IRBM, 43 (2022), 87–92.
https://doi.org/10.1016/j.irbm.2020.05.003
60. X. Fan, X. Feng, Y. Dong, H. Hou, COVID-19 CT image recognition algorithm based on
transformer and CNN, Displays, 72 (2022), 102150. https://doi.org/10.1016/j.displa.2022.102150
61. A. S. Ebenezer, S. D. Kanmani, M. Sivakumar, S. J. Priya, Effect of image transformation on
EfficientNet model for COVID-19 CT image classification, Mater. Today Proc., 51 (2022), 2512–
2519. https://doi.org/10.1016/j.matpr.2021.12.121
62. N. S. Shaik, T. K. Cherukuri, Transfer learning based novel ensemble classifier for
COVID-19 detection from chest CT-scans, Comput. Biol. Med., 141 (2022), 105127.
https://doi.org/10.1016/j.compbiomed.2021.105127