covid 19 image classification

The proposed CNN architecture for Task 2 consists of 14 weighted layers, in which there are three convolutional layers and one fully connected layer, as shown in Fig. COVID-19 image classification using deep features and fractional-order marine predators algorithm. Eurosurveillance 18, 20503 (2013). In54, AlexNet pre-trained network was used to extract deep features then applied PCA to select the best features by eliminating highly correlated features. The family of coronaviruses is considered serious pathogens for people because they infect respiratory, hepatic, gastrointestinal, and neurologic diseases. The combination of Conv. Deep residual learning for image recognition. 0.9875 and 0.9961 under binary and multi class classifications respectively. A combination of fractional-order and marine predators algorithm (FO-MPA) is considered an integration among a robust tool in mathematics named fractional-order calculus (FO). In order to normalize the values between 0 and 1 by dividing by the sum of all feature importance values, as in Eq. This study presents an investigation on 16 pretrained CNNs for classification of COVID-19 using a large public database of CT scans collected from COVID-19 patients and non-COVID-19 subjects. A.A.E. Memory FC prospective concept (left) and weibull distribution (right). 132, 8198 (2018). The GL in the discrete-time form can be modeled as below: where T is the sampling period, and m is the length of the memory terms (memory window). Coronavirus Disease (COVID-19): A primer for emergency physicians (2020) Summer Chavez et al. Using the best performing fine-tuned VGG-16 DTL model, tests were carried out on 470 unlabeled image dataset, which was not used in the model training and validation processes. Etymology. In this paper, we used TPUs for powerful computation, which is more appropriate for CNN. Stage 2 has been executed in the second third of the total number of iterations when \(\frac{1}{3}t_{max}< t< \frac{2}{3}t_{max}\). 111, 300323. For both datasets, the Covid19 images were collected from patients with ages ranging from 40-84 from both genders. Deep cnns for microscopic image classification by exploiting transfer learning and feature concatenation. Comput. wrote the intro, related works and prepare results. In this paper, after applying Chi-square, the feature vector is minimized for both datasets from 51200 to 2000. . J. Med. The COVID-19 pandemic has been having a severe and catastrophic effect on humankind and is being considered the most crucial health calamity of the century. (18)(19) for the second half (predator) as represented below. In this paper, a new ML-method proposed to classify the chest x-ray images into two classes, COVID-19 patient or non-COVID-19 person. Biomed. These datasets contain hundreds of frontal view X-rays and considered the largest public resource for COVID-19 image data. Harris hawks optimization: algorithm and applications. A properly trained CNN requires a lot of data and CPU/GPU time. Med. 40, 2339 (2020). 11314, 113142S (International Society for Optics and Photonics, 2020). As a result, the obtained outcomes outperformed previous works in terms of the models general performance measure. Robertas Damasevicius. The Shearlet transform FS method showed better performances compared to several FS methods. The proposed IMF approach successfully achieves two important targets, selecting small feature numbers with high accuracy. Nature 503, 535538 (2013). How- individual class performance. Slider with three articles shown per slide. & Wang, W. Medical image segmentation using fruit fly optimization and density peaks clustering. Diagnosis of parkinsons disease with a hybrid feature selection algorithm based on a discrete artificial bee colony. Negative COVID-19 images were collected from another Chest X-ray Kaggle published dataset43. Syst. Thank you for visiting nature.com. Article Mirjalili, S., Mirjalili, S. M. & Lewis, A. Grey wolf optimizer. The memory terms of the prey are updated at the end of each iteration based on first in first out concept. where \(ni_{j}\) is the importance of node j, while \(w_{j}\) refers to the weighted number of samples reaches the node j, also \(C_{j}\) determines the impurity value of node j. left(j) and right(j) are the child nodes from the left split and the right split on node j, respectively. Imag. Therefore, in this paper, we propose a hybrid classification approach of COVID-19. A. Transmission scenarios for middle east respiratory syndrome coronavirus (mers-cov) and how to tell them apart. Li, S., Chen, H., Wang, M., Heidari, A. More so, a combination of partial differential equations and deep learning was applied for medical image classification by10. 2 (left). 97, 849872 (2019). In this paper, different Conv. Also, they require a lot of computational resources (memory & storage) for building & training. 2 (right). In some cases (as exists in this work), the dataset is limited, so it is not sufficient for building & training a CNN. On the second dataset, dataset 2 (Fig. Access through your institution. 43, 302 (2019). In this work, we have used four transfer learning models, VGG16, InceptionV3, ResNet50, and DenseNet121 for the classification tasks. For general case based on the FC definition, the Eq. Also, in12, an Fs method based on SVM was proposed to detect Alzheimers disease from SPECT images. Cauchemez, S. et al. Ozturk, T. et al. In this paper, we propose an improved hybrid classification approach for COVID-19 images by combining the strengths of CNNs (using a powerful architecture called Inception) to extract features and . The predator tries to catch the prey while the prey exploits the locations of its food. They used different images of lung nodules and breast to evaluate their FS methods. Use the Previous and Next buttons to navigate the slides or the slide controller buttons at the end to navigate through each slide. Li et al.36 proposed an FS method using a discrete artificial bee colony (ABC) to improve the classification of Parkinsons disease. As seen in Fig. Metric learning Metric learning can create a space in which image features within the. It also shows that FO-MPA can select the smallest subset of features, which reflects positively on performance. Although the performance of the MPA and bGWO was slightly similar, the performance of SGA and WOA were the worst in both max and min measures. 4 and Table4 list these results for all algorithms. Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article. In this experiment, the selected features by FO-MPA were classified using KNN. For each decision tree, node importance is calculated using Gini importance, Eq. Softw. They shared some parameters, such as the total number of iterations and the number of agents which were set to 20 and 15, respectively. As Inception examines all X-ray images over and over again in each epoch during the training, these rapid ups and downs are slowly minimized in the later part of the training. Very deep convolutional networks for large-scale image recognition. Automated detection of covid-19 cases using deep neural networks with x-ray images. Isolation and characterization of a bat sars-like coronavirus that uses the ace2 receptor. }\delta (1-\delta )(2-\delta )(3-\delta ) U_{i}(t-3) + P.R\bigotimes S_i. . MPA simulates the main aim for most creatures that is searching for their foods, where a predator contiguously searches for food as well as the prey. The whole dataset contains around 200 COVID-19 positive images and 1675 negative COVID19 images. Howard, A.G. etal. 1. Article Also, WOA algorithm showed good results in all measures, unlike dataset 1, which can conclude that no algorithm can solve all kinds of problems. The proposed segmentation method is capable of dealing with the problem of diffuse lung borders in CXR images of patients with COVID-19 severe or critical. Hashim, F. A., Houssein, E. H., Mabrouk, M. S., Al-Atabany, W. & Mirjalili, S. Henry gas solubility optimization: a novel physics-based algorithm. According to the formula10, the initial locations of the prey and predator can be defined as below: where the Elite matrix refers to the fittest predators. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 19 (2015). J. Covid-19 Classification Using Deep Learning in Chest X-Ray Images Abstract: Covid-19 virus, which has emerged in the Republic of China in an undetermined cause, has affected the whole world quickly. The experimental results and comparisons with other works are presented inResults and discussion section, while they are discussed in Discussion section Finally, the conclusion is described in Conclusion section. and M.A.A.A. Finally, the predator follows the levy flight distribution to exploit its prey location. 25, 3340 (2015). Dhanachandra and Chanu35 proposed a hybrid method of dynamic PSO and fuzzy c-means to segment two types of medical images, MRI and synthetic images. We adopt a special type of CNN called a pre-trained model where the network is previously trained on the ImageNet dataset, which contains millions of variety of images (animal, plants, transports, objects,..) on 1000 classe categories. https://doi.org/10.1155/2018/3052852 (2018). Int. A NOVEL COMPARATIVE STUDY FOR AUTOMATIC THREE-CLASS AND FOUR-CLASS COVID-19 CLASSIFICATION ON X-RAY IMAGES USING DEEP LEARNING: Authors: Yaar, H. Ceylan, M. Keywords: Convolutional neural networks Covid-19 Deep learning Densenet201 Inceptionv3 Local binary pattern Local entropy X-ray chest classification Xception: Issue Date: 2022: Publisher: Fractional Differential Equations: An Introduction to Fractional Derivatives, Fdifferential Equations, to Methods of their Solution and Some of Their Applications Vol. Eur. 11, 243258 (2007). Image Anal. \end{aligned}$$, $$\begin{aligned} U_i(t+1)-U_i(t)=P.R\bigotimes S_i \end{aligned}$$, $$\begin{aligned} D ^{\delta } \left[ U_{i}(t+1)\right] =P.R\bigotimes S_i \end{aligned}$$, $$D^{\delta } \left[ {U_{i} (t + 1)} \right] = U_{i} (t + 1) + \sum\limits_{{k = 1}}^{m} {\frac{{( - 1)^{k} \Gamma (\delta + 1)U_{i} (t + 1 - k)}}{{\Gamma (k + 1)\Gamma (\delta - k + 1)}}} = P \cdot R \otimes S_{i} .$$, $$\begin{aligned} \begin{aligned} U(t+1)_{i}= - \sum _{k=1}^{m} \frac{(-1)^k\Gamma (\delta +1)U_{i}(t+1-k)}{\Gamma (k+1)\Gamma (\delta -k+1)} + P.R\bigotimes S_i. Use of chest ct in combination with negative rt-pcr assay for the 2019 novel coronavirus but high clinical suspicion. Feature selection based on gaussian mixture model clustering for the classification of pulmonary nodules based on computed tomography. SMA is on the second place, While HGSO, SCA, and HHO came in the third to fifth place, respectively. 92, 103662. https://doi.org/10.1016/j.engappai.2020.103662 (2020). Then, using an enhanced version of Marine Predators Algorithm to select only relevant features. Some people say that the virus of COVID-19 is. In Eq. In general, MPA is a meta-heuristic technique that simulates the behavior of the prey and predator in nature37. They used K-Nearest Neighbor (kNN) to classify x-ray images collected from Montgomery dataset, and it showed good performances. Bisong, E. Building Machine Learning and Deep Learning Models on Google Cloud Platform (Springer, Berlin, 2019). Decaf: A deep convolutional activation feature for generic visual recognition. The proposed cascaded system is proposed to segment the lung, detect, localize, and quantify COVID-19 infections from computed tomography images, which can reliably localize infections of various shapes and sizes, especially small infection regions, which are rarely considered in recent studies. Havaei, M. et al. \(Fit_i\) denotes a fitness function value. So, there might be sometimes some conflict issues regarding the features vector file types or issues related to storage capacity and file transferring. FP (false positives) are the positive COVID-19 images that were incorrectly labeled as negative COVID-19, while FN (false negatives) are the negative COVID-19 images that were mislabeled as positive COVID-19 images. The two datasets consist of X-ray COVID-19 images by international Cardiothoracic radiologist, researchers and others published on Kaggle. Scientific Reports (Sci Rep) }, \end{aligned}$$, $$\begin{aligned} D^{\delta }[U(t)]=\frac{1}{T^\delta }\sum _{k=0}^{m} \frac{(-1)^k\Gamma (\delta +1)U(t-kT)}{\Gamma (k+1)\Gamma (\delta -k+1)} \end{aligned}$$, $$\begin{aligned} D^1[U(t)]=U(t+1)-U(t) \end{aligned}$$, $$\begin{aligned} U=Lower+rand_1\times (Upper - Lower ) \end{aligned}$$, $$\begin{aligned} Elite=\left[ \begin{array}{cccc} U_{11}^1&{}U_{12}^1&{}\ldots &{}U_{1d}^1\\ U_{21}^1&{}U_{22}^1&{}\ldots &{}U_{2d}^1\\ \ldots &{}\ldots &{}\ldots &{}\ldots \\ U_{n1}^1&{}U_{n2}^1&{}\ldots &{}U_{nd}^1\\ \end{array}\right] , \, U=\left[ \begin{array}{cccc} U_{11}&{}U_{12}&{}\ldots &{}U_{1d}\\ U_{21}&{}U_{22}&{}\ldots &{}U_{2d}\\ \ldots &{}\ldots &{}\ldots &{}\ldots \\ U_{n1}&{}U_{n2}&{}\ldots &{}U_{nd}\\ \end{array}\right] , \, \end{aligned}$$, $$\begin{aligned} S_i&= {} R_B \bigotimes (Elite_i-R_B\bigotimes U_i), i=1,2,\ldots ,n \end{aligned}$$, $$\begin{aligned} U_i&= {} U_i+P.R\bigotimes S_i \end{aligned}$$, \(\frac{1}{3}t_{max}< t< \frac{2}{3}t_{max}\), $$\begin{aligned} S_i&= {} R_L \bigotimes (Elite_i-R_L\bigotimes U_i), i=1,2,\ldots ,n/2 \end{aligned}$$, $$\begin{aligned} S_i&= {} R_B \bigotimes (R_B \bigotimes Elite_i- U_i), i=1,2,\ldots ,n/2 \end{aligned}$$, $$\begin{aligned} U_i&= {} Elite_i+P.CF\bigotimes S_i,\, CF= \left( 1-\frac{t}{t_{max}} \right) ^{\left(2\frac{t}{t_{max}}\right) } \end{aligned}$$, $$\begin{aligned} S_i&= {} R_L \bigotimes (R_L \bigotimes Elite_i- U_i), i=1,2,\ldots ,n \end{aligned}$$, $$\begin{aligned} U_i&= {} Elite_i+P.CF\bigotimes S_i,\, CF= \left( 1-\frac{t}{t_{max}}\right) ^{\left(2\frac{t}{t_{max}} \right) } \end{aligned}$$, $$\begin{aligned} U_i=\left\{ \begin{array}{ll} U_i+CF [U_{min}+R \bigotimes (U_{max}-U_{min})]\bigotimes W &{} r_5 < FAD \\ U_i+[FAD(1-r)+r](U_{r1}-U_{r2}) &{} r_5 > FAD\\ \end{array}\right. The test accuracy obtained for the model was 98%. & Mahmoud, N. Feature selection based on hybrid optimization for magnetic resonance imaging brain tumor classification and segmentation. FC provides a clear interpretation of the memory and hereditary features of the process. Accordingly, the prey position is upgraded based the following equations. Lett. In this paper, we proposed a novel COVID-19 X-ray classification approach, which combines a CNN as a sufficient tool to extract features from COVID-19 X-ray images. Rajpurkar, P. etal. For the special case of \(\delta = 1\), the definition of Eq. Tree based classifier are the most popular method to calculate feature importance to improve the classification since they have high accuracy, robustness, and simple38. (33)), showed that FO-MPA also achieved the best value of the fitness function compared to others. CAS It is calculated between each feature for all classes, as in Eq. Based on Standard Deviation measure (STD), the most stable algorithms were SCA, SGA, BPSO, and bGWO, respectively. Moreover, a multi-objective genetic algorithm was applied to search for the optimal features subset. Adv. All authors discussed the results and wrote the manuscript together. Automatic COVID-19 lung images classification system based on convolution neural network. https://keras.io (2015). Furthermore, the proposed GRAY+GRAY_HE+GRAY_CLAHE image representation was evaluated on two different datasets, SARS-CoV-2 CT-Scan and New_Data_CoV2, where it was found to be superior to RGB . Average of the consuming time and the number of selected features in both datasets. In the last two decades, two famous types of coronaviruses SARS-CoV and MERS-CoV had been reported in 2003 and 2012, in China, and Saudi Arabia, respectively3. The algorithm combines the assessment of image quality, digital image processing and deep learning for segmentation of the lung tissues and their classification. MRFGRO: a hybrid meta-heuristic feature selection method for screening COVID-19 using deep features, Detection and analysis of COVID-19 in medical images using deep learning techniques, Cov-caldas: A new COVID-19 chest X-Ray dataset from state of Caldas-Colombia, Deep learning in veterinary medicine, an approach based on CNN to detect pulmonary abnormalities from lateral thoracic radiographs in cats, COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images, ANFIS-Net for automatic detection of COVID-19, A multi-scale gated multi-head attention depthwise separable CNN model for recognizing COVID-19, Validating deep learning inference during chest X-ray classification for COVID-19 screening, Densely attention mechanism based network for COVID-19 detection in chest X-rays, https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports/, https://github.com/ieee8023/covid-chestxray-dataset, https://stanfordmlgroup.github.io/projects/chexnet, https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia, https://www.sirm.org/en/category/articles/covid-19-database/, https://drive.google.com/file/d/1-oK-eeEgdCMCnykH364IkAK3opmqa9Rvasx/view?usp=sharing, https://doi.org/10.1016/j.irbm.2019.10.006, https://research.googleblog.com/2017/11/automl-for-large-scaleimage.html, https://doi.org/10.1016/j.engappai.2020.103662, https://www.sirm.org/category/senza-categoria/covid-19/, https://doi.org/10.1016/j.future.2020.03.055, http://creativecommons.org/licenses/by/4.0/, Skin cancer detection using ensemble of machine learning and deep learning techniques, Plastic pollution induced by the COVID-19: Environmental challenges and outlook, An Inclusive Survey on Marine Predators Algorithm: Variants andApplications, A Multi-strategy Improved Outpost and Differential Evolution Mutation Marine Predators Algorithm for Global Optimization, A light-weight convolutional Neural Network Architecture for classification of COVID-19 chest X-Ray images. Furthermore, using few hundreds of images to build then train Inception is considered challenging because deep neural networks need large images numbers to work efficiently and produce efficient features. implemented the FO-MPA swarm optimization and prepared the related figures and manuscript text. However, the modern name is tenggiling.In Javanese it is terenggiling; and in the Philippine languages, it is goling, tanggiling, or balintong (with the same meaning).. To evaluate the performance of the proposed model, we computed the average of both best values and the worst values (Max) as well as STD and computational time for selecting features. Radiomics: extracting more information from medical images using advanced feature analysis. Objective: To help improve radiologists' efficacy of disease diagnosis in reading computed tomography (CT) images, this study aims to investigate the feasibility of applying a modified deep learning (DL) method as a new strategy to automatically segment disease-infected regions and predict disease severity. Cite this article. Also, all other works do not give further statistics about their models complexity and the number of featurset produced, unlike, our approach which extracts the most informative features (130 and 86 features for dataset 1 and dataset 2) that imply faster computation time and, accordingly, lower resource consumption. In57, ResNet-50 CNN has been applied after applying horizontal flipping, random rotation, random zooming, random lighting, and random wrapping on raw images. The Softmax activation function is used for this purpose because the output should be binary (positive COVID-19 negative COVID-19). Since its structure consists of some parallel paths, all the paths use padding of 1 pixel to preserve the same height & width for the inputs and the outputs. implemented the deep neural networks and classification as well as prepared the related figures and manuscript text. Comput. volume10, Articlenumber:15364 (2020) Syst. Med. Podlubny, I. As seen in Table1, we keep the last concatenation layer which contains the extracted features, so we removed the top layers such as the Flatten, Drop out and the Dense layers which the later performs classification (named as FC layer). HIGHLIGHTS who: Qinghua Xie and colleagues from the Te Afliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, Hunan, China have published the Article: Automatic Segmentation and Classification for Antinuclear Antibody Images Based on Deep Learning, in the Journal: Computational Intelligence and Neuroscience of 14/08/2022 what: Terefore, the authors . For instance,\(1\times 1\) conv. is applied before larger sized kernels are applied to reduce the dimension of the channels, which accordingly, reduces the computation cost. For example, Da Silva et al.30 used the genetic algorithm (GA) to develop feature selection methods for ranking the quality of medical images. However, the proposed FO-MPA approach has an advantage in performance compared to other works. and JavaScript. Therefore, several pre-trained models have won many international image classification competitions such as VGGNet24, Resnet25, Nasnet26, Mobilenet27, Inception28 and Xception29. Based on54, the later step reduces the memory requirements, and improve the efficiency of the framework. In this paper, we used two different datasets. Faramarzi et al.37 divided the agents for two halves and formulated Eqs. With accounting the first four previous events (\(m=4\)) from the memory data with derivative order \(\delta\), the position of prey can be modified as follow; Second: Adjusting \(R_B\) random parameter based on weibull distribution. Eng. The data was collected mainly from retrospective cohorts of pediatric patients from Guangzhou Women and Childrens medical center. PubMedGoogle Scholar. (2) To extract various textural features using the GLCM algorithm. 51, 810820 (2011). Deep learning plays an important role in COVID-19 images diagnosis. In this work, the MPA is enhanced by fractional calculus memory feature, as a result, Fractional-order Marine Predators Algorithm (FO-MPA) is introduced. Afzali et al.15 proposed an FS method based on principal component analysis and contour-based shape descriptors to detect Tuberculosis from lung X-Ray Images. Technol. Imaging Syst. The proposed approach selected successfully 130 and 86 out of 51 K features extracted by inception from dataset 1 and dataset 2, while improving classification accuracy at the same time. They are distributed among people, bats, mice, birds, livestock, and other animals1,2. In our example the possible classifications are covid, normal and pneumonia. In Proceedings of the IEEE Conference on computer vision and pattern recognition workshops, 806813 (2014). The symbol \(R_B\) refers to Brownian motion. (14)(15) to emulate the motion of the first half of the population (prey) and Eqs. However, some of the extracted features by CNN might not be sufficient, which may affect negatively the quality of the classification images. https://doi.org/10.1016/j.future.2020.03.055 (2020). Biol. We do not present a usable clinical tool for COVID-19 diagnosis, but offer a new, efficient approach to optimize deep learning-based architectures for medical image classification purposes. It noted that all produced feature vectors by CNNs used in this paper are at least bigger by more than 300 times compared to that produced by FO-MPA in terms of the size of the featureset. CAS HGSO was ranked second with 146 and 87 selected features from Dataset 1 and Dataset 2, respectively. Book 78, 2091320933 (2019). Generally, the most stable algorithms On dataset 1 are WOA, SCA, HGSO, FO-MPA, and SGA, respectively. Vis. (8) at \(T = 1\), the expression of Eq. Furthermore, deep learning using CNN is considered one of the best choices in medical imaging applications20, especially classification. Detection of lung cancer on chest ct images using minimum redundancy maximum relevance feature selection method with convolutional neural networks. They compared the BA to PSO, and the comparison outcomes showed that BA had better performance. Imaging 35, 144157 (2015). Such methods might play a significant role as a computer-aided tool for image-based clinical diagnosis soon. Deep-learning artificial intelligent (AI) methods have the potential to help improve diagnostic efficiency and accuracy for reading portable CXRs. PubMed Central Mobilenets: Efficient convolutional neural networks for mobile vision applications. A deep feature learning model for pneumonia detection applying a combination of mRMR feature selection and machine learning models. Med. In 2019 26th National and 4th International Iranian Conference on Biomedical Engineering (ICBME), 194198 (IEEE, 2019). After feature extraction, we applied FO-MPA to select the most significant features. Propose similarity regularization for improving C. The MPA starts with the initialization phase and then passing by other three phases with respect to the rational velocity among the prey and the predator. Math. Decis. Besides, all algorithms showed the same statistical stability in STD measure, except for HHO and HGSO. Li, J. et al. In 2018 IEEE International Symposium on Circuits and Systems (ISCAS), 15 (IEEE, 2018). The dataset consists of 21,165 chest X-ray (CXR) COVID-19 images distributed on four categories which are COVID19, lung opacity, viral pneumonia, and NORMAL (Non-COVID). Our proposed approach is called Inception Fractional-order Marine Predators Algorithm (IFM), where we combine Inception (I) with Fractional-order Marine Predators Algorithm (FO-MPA). In our experiment, we randomly split the data into 70%, 10%, and 20% for training, validation, and testing sets, respectively. It can be concluded that FS methods have proven their advantages in different medical imaging applications19. arXiv preprint arXiv:1704.04861 (2017). (8) can be remodeled as below: where \(D^1[x(t)]\) represents the difference between the two followed events. PubMed 2022 May;144:105350. doi: 10.1016/j.compbiomed.2022.105350. Al-qaness, M. A., Ewees, A. The Weibull Distribution is a heavy-tied distribution which presented as in Fig. Computational image analysis techniques play a vital role in disease treatment and diagnosis. (9) as follows. Moreover, we design a weighted supervised loss that assigns higher weight for . 121, 103792 (2020). This paper reviews the recent progress of deep learning in COVID-19 images applications from five aspects; Firstly, 33 COVID-19 datasets and data enhancement methods are introduced; Secondly, COVID-19 classification methods . Stage 1: After the initialization, the exploration phase is implemented to discover the search space. Therefore, reducing the size of the feature from about 51 K as extracted by deep neural networks (Inception) to be 128.5 and 86 in dataset 1 and dataset 2, respectively, after applying FO-MPA algorithm while increasing the general performance can be considered as a good achievement as a machine learning goal. Experimental results have shown that the proposed Fuzzy Gabor-CNN algorithm attains highest accuracy, Precision, Recall and F1-score when compared to existing feature extraction and classification techniques. a cough chills difficulty breathing tiredness body aches headaches a new loss of taste or smell a sore throat nausea and vomiting diarrhea Not everyone with COVID-19 develops all of these. Syst. and A.A.E. Also, other recent published works39, who combined a CNN architecture with Weighted Symmetric Uncertainty (WSU) to select optimal features for traffic classification. Afzali, A., Mofrad, F.B. Future Gener. (1): where \(O_k\) and \(E_k\) refer to the actual and the expected feature value, respectively. (22) can be written as follows: By using the discrete form of GL definition of Eq. arXiv preprint arXiv:2003.13145 (2020). & SHAH, S. S.H. The diagnostic evaluation of convolutional neural network (cnn) for the assessment of chest x-ray of patients infected with covid-19. Acharya et al.11 applied different FS methods to classify Alzheimers disease using MRI images. 9, 674 (2020). In COVID19 triage, DB-YNet is a promising tool to assist physicians in the early identification of COVID19 infected patients for quick clinical interventions. PVT-COV19D: COVID-19 Detection Through Medical Image Classification Based on Pyramid Vision Transformer. The predator uses the Weibull distribution to improve the exploration capability. Figure5, shows that FO-MPA shows an efficient and faster convergence than the other optimization algorithms on both datasets. Google Scholar. Also, in58 a new CNN architecture called EfficientNet was proposed, where more blocks were added on top of the model after applying normalization of images pixels intensity to the range (0 to 1). To survey the hypothesis accuracy of the models. In Inception, there are different sizes scales convolutions (conv. This study aims to improve the COVID-19 X-ray image classification using feature selection technique by the regression mutual information deep convolution neuron networks (RMI Deep-CNNs). In this paper, each feature selection algorithm were exposed to select the produced feature vector from Inception aiming at selecting only the most relevant features. For each of these three categories, there is a number of patients and for each of them, there is a number of CT scan images correspondingly.

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