covid 19 image classificationcovid 19 image classification

Appl. Extensive evaluation experiments had been carried out with a collection of two public X-ray images datasets. Also, in12, an Fs method based on SVM was proposed to detect Alzheimers disease from SPECT images. For instance,\(1\times 1\) conv. used a dark Covid-19 network for multiple classification experiments on Covid-19 with an accuracy of 87% [ 23 ]. Provided by the Springer Nature SharedIt content-sharing initiative, Environmental Science and Pollution Research (2023), Archives of Computational Methods in Engineering (2023), Arabian Journal for Science and Engineering (2023). 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). Get the most important science stories of the day, free in your inbox. Therefore in MPA, for the first third of the total iterations, i.e., \(\frac{1}{3}t_{max}\)). https://www.sirm.org/category/senza-categoria/covid-19/ (2020). Inf. Its structure is designed based on experts' knowledge and real medical process. Toaar, M., Ergen, B. et al. The authors declare no competing interests. The name "pangolin" comes from the Malay word pengguling meaning "one who rolls up" from guling or giling "to roll"; it was used for the Sunda pangolin (Manis javanica). 2. Figure3 illustrates the structure of the proposed IMF approach. (22) can be written as follows: By using the discrete form of GL definition of Eq. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition12511258 (2017). Regarding the consuming time as in Fig. 11, 243258 (2007). Article \delta U_{i}(t)+ \frac{1}{2! Early diagnosis, timely treatment, and proper confinement of the infected patients are some possible ways to control the spreading of . Litjens, G. et al. 132, 8198 (2018). They achieved 98.08 % and 96.51 % of accuracy and F-Score, respectively compared to our approach with 98.77 % and 98.2% for accuracy and F-Score, respectively. It classifies the chest X-ray images into three categories that includes Covid-19, Pneumonia and normal. PubMed Central All data used in this paper is available online in the repository, [https://github.com/ieee8023/covid-chestxray-dataset], [https://stanfordmlgroup.github.io/projects/chexnet], [https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia] and [https://www.sirm.org/en/category/articles/covid-19-database/]. For diagnosing COVID-19, the RT-PCR (real-time polymerase chain reaction) is a standard diagnostic test, but, it can be considered as a time-consuming test, more so, it also suffers from false negative diagnosing4. 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. The different proposed models will be trained with three-class balanced dataset which consists of 3000 images, 1000 images for each class. We are hiring! PubMedGoogle Scholar. (1): where \(O_k\) and \(E_k\) refer to the actual and the expected feature value, respectively. where \(R_L\) has random numbers that follow Lvy distribution. In this experiment, the selected features by FO-MPA were classified using KNN. On January 20, 2023, Japanese Prime Minister Fumio Kishida announced that the country would be downgrading the COVID-19 classification. 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. Machine learning (ML) methods can play vital roles in identifying COVID-19 patients by visually analyzing their chest x-ray images. 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. 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. This means we can use pre-trained model weights, leveraging all of the time and data it took for training the convolutional part, and just remove the FCN layer. 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 . Toaar, M., Ergen, B. One of the best methods of detecting. In this paper, after applying Chi-square, the feature vector is minimized for both datasets from 51200 to 2000. Radiomics: extracting more information from medical images using advanced feature analysis. & Dai, Q. Discriminative clustering and feature selection for brain mri segmentation. To obtain In this paper, filters of size 2, besides a stride of 2 and \(2 \times 2\) as Max pool, were adopted. Two real datasets about COVID-19 patients are studied in this paper. Both datasets shared some characteristics regarding the collecting sources. }\delta (1-\delta )(2-\delta ) U_{i}(t-2)\\&\quad + \frac{1}{4! (2) To extract various textural features using the GLCM algorithm. This stage can be mathematically implemented as below: In Eq. MATH We build the first Classification model using VGG16 Transfer leaning framework and second model using Deep Learning Technique Convolutional Neural Network CNN to classify and diagnose the disease and we able to achieve the best accuracy in both the model. \end{aligned} \end{aligned}$$, $$\begin{aligned} \begin{aligned} U_{i}(t+1)&= \frac{1}{1!} Cite this article. After feature extraction, we applied FO-MPA to select the most significant features. Article They were manually aggregated from various web based repositories into a machine learning (ML) friendly format with accompanying data loader code. In general, feature selection (FS) methods are widely employed in various applications of medical imaging applications. Comput. Cohen, J.P., Morrison, P. & Dao, L. Covid-19 image data collection. 42, 6088 (2017). Automated detection of covid-19 cases using deep neural networks with x-ray images. Intell. J. In this subsection, a comparison with relevant works is discussed. Adv. The \(\delta\) symbol refers to the derivative order coefficient. In ancient India, according to Aelian, it was . Performance analysis of neural networks for classification of medical images with wavelets as a feature extractor. Fung, G. & Stoeckel, J. Svm feature selection for classification of spect images of alzheimers disease using spatial information. Chollet, F. Keras, a python deep learning library. The predator tries to catch the prey while the prey exploits the locations of its food. arXiv preprint arXiv:1409.1556 (2014). A features extraction method using the Histogram of Oriented Gradients (HOG) algorithm and the Linear Support Vector Machine (SVM), K-Nearest Neighbor (KNN) Medium and Decision Tree (DT) Coarse Tree classification methods can be used in the diagnosis of Covid-19 disease. A. HIGHLIGHTS who: Yuan Jian and Qin Xiao from the Fukuoka University, Japan have published the Article: Research and Application of Fine-Grained Image Classification Based on Small Collar Dataset, in the Journal: (JOURNAL) what: MC-Loss drills down on the channels to effectively navigate the model, focusing on different distinguishing regions and highlighting diverse features. Although outbreaks of SARS and MERS had confirmed human to human transmission3, they had not the same spread speed and infection power of the new coronavirus (COVID-19). The following stage was to apply Delta variants. Softw. The announcement confirmed that from May 8, following Japan's Golden Week holiday period, COVID-19 will be officially downgraded to Class 5, putting the virus on the same classification level as seasonal influenza. The data was collected mainly from retrospective cohorts of pediatric patients from Guangzhou Women and Childrens medical center. 7, most works are pre-prints for two main reasons; COVID-19 is the most recent and trend topic; also, there are no sufficient datasets that can be used for reliable results. On the second dataset, dataset 2 (Fig. Chong, D. Y. et al. \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. They also used the SVM to classify lung CT images. Figure7 shows the most recent published works as in54,55,56,57 and44 on both dataset 1 and dataset 2. Imaging 35, 144157 (2015). Use the Previous and Next buttons to navigate the slides or the slide controller buttons at the end to navigate through each slide. Inf. Continuing on my commitment to share small but interesting things in Google Cloud, this time I created a model for a The first one, dataset 1 was collected by Joseph Paul Cohen and Paul Morrison and Lan Dao42, where some COVID-19 images were collected by an Italian Cardiothoracic radiologist. 9, 674 (2020). Computer Department, Damietta University, Damietta, Egypt, Electrical Engineering Department, Faculty of Engineering, Fayoum University, Fayoum, Egypt, State Key Laboratory for Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan, China, Department of Applied Informatics, Vytautas Magnus University, Kaunas, Lithuania, Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, Egypt, School of Computer Science and Robotics, Tomsk Polytechnic University, Tomsk, Russia, You can also search for this author in Eng. Sahlol, A.T., Yousri, D., Ewees, A.A. et al. Fractional-order calculus (FC) gains the interest of many researchers in different fields not only in the modeling sectors but also in developing the optimization algorithms. SMA is on the second place, While HGSO, SCA, and HHO came in the third to fifth place, respectively. Civit-Masot et al. The results of max measure (as in Eq. & Cmert, Z. (2) calculated two child nodes. Figure6 shows a comparison between our FO-MPA approach and other CNN architectures. Sci Rep 10, 15364 (2020). EMRes-50 model . Then, using an enhanced version of Marine Predators Algorithm to select only relevant features. The MCA-based model is used to process decomposed images for further classification with efficient storage. 10, 10331039 (2020). 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. The whale optimization algorithm. \(r_1\) and \(r_2\) are the random index of the prey. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. This combination should achieve two main targets; high performance and resource consumption, storage capacity which consequently minimize processing time. J. Clin. Biomed. In some cases (as exists in this work), the dataset is limited, so it is not sufficient for building & training a CNN. In this paper, we used two different datasets. Lilang Zheng, Jiaxuan Fang, Xiaorun Tang, Hanzhang Li, Jiaxin Fan, Tianyi Wang, Rui Zhou, Zhaoyan Yan: PVT-COV19D: COVID-19 Detection Through Medical Image Classification Based on Pyramid Vision Transformer. Accordingly, that reflects on efficient usage of memory, and less resource consumption. ), such as \(5\times 5\), \(3 \times 3\), \(1 \times 1\). Szegedy, C. et al. }\delta (1-\delta ) U_{i}(t-1)+ \frac{1}{3! }, \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. Moreover, other COVID-19 positive images were added by the Italian Society of Medical and Interventional Radiology (SIRM) COVID-19 Database45. Eng. The prey follows Weibull distribution during discovering the search space to detect potential locations of its food. To further analyze the proposed algorithm, we evaluate the selected features by FO-MPA by performing classification. Whereas, FO-MPA, MPA, HGSO, and WOA showed similar STD results. 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). The survey asked participants to broadly classify the findings of each chest CT into one of the four RSNA COVID-19 imaging categories, then select which imaging features led to their categorization. By achieving 98.7%, 98.2% and 99.6%, 99% of classification accuracy and F-Score for dataset 1 and dataset 2, respectively, the proposed approach outperforms several CNNs and all recent works on COVID-19 images. Coronavirus Disease (COVID-19): A primer for emergency physicians (2020) Summer Chavez et al. New Images of Novel Coronavirus SARS-CoV-2 Now Available NIAID Now | February 13, 2020 This scanning electron microscope image shows SARS-CoV-2 (yellow)also known as 2019-nCoV, the virus that causes COVID-19isolated from a patient in the U.S., emerging from the surface of cells (blue/pink) cultured in the lab. 92, 103662. https://doi.org/10.1016/j.engappai.2020.103662 (2020). Scientific Reports Volume 10, Issue 1, Pages - Publisher. However, using medical imaging, chest CT, and chest X-ray scan can play a critical role in COVID-19 diagnosis. Improving the ranking quality of medical image retrieval using a genetic feature selection method. Technol. The code of the proposed approach is also available via the following link [https://drive.google.com/file/d/1-oK-eeEgdCMCnykH364IkAK3opmqa9Rvasx/view?usp=sharing]. For general case based on the FC definition, the Eq. However, it was clear that VGG19 and MobileNet achieved the best performance over other CNNs. Also, image segmentation can extract critical features, including the shape of tissues, and texture5,6. Article (23), the general formulation for the solutions of FO-MPA based on FC memory perspective can be written as follows: After checking the previous formula, it can be detected that the motion of the prey becomes based on some terms from the previous solutions with a length of (m), as depicted in Fig. The two datasets consist of X-ray COVID-19 images by international Cardiothoracic radiologist, researchers and others published on Kaggle. Recently, a combination between the fractional calculus tool and the meta-heuristics opens new doors in providing robust and reliable variants41. Eng. Nevertheless, a common mistake in COVID-19 dataset fusion, mainly on classification tasks, is that by mixing many datasets of COVID-19 and using as Control images another dataset, there will be . Therefore, several pre-trained models have won many international image classification competitions such as VGGNet24, Resnet25, Nasnet26, Mobilenet27, Inception28 and Xception29. For both datasets, the Covid19 images were collected from patients with ages ranging from 40-84 from both genders. Bisong, E. Building Machine Learning and Deep Learning Models on Google Cloud Platform (Springer, Berlin, 2019). COVID-19 is the most transmissible disease, caused by the SARS-CoV-2 virus that severely infects the lungs and the upper respiratory tract of the human body.This virus badly affected the lives and wellness of millions of people worldwide and spread widely. For each decision tree, node importance is calculated using Gini importance, Eq. The definitions of these measures are as follows: where TP (true positives) refers to the positive COVID-19 images that were correctly labeled by the classifier, while TN (true negatives) is the negative COVID-19 images that were correctly labeled by the classifier. 35, 1831 (2017). Image Anal. As seen in Table3, on Dataset 1, the FO-MPA outperformed the other algorithms in the mean of fitness value as it achieved the smallest average fitness function value followed by SMA, HHO, HGSO, SCA, BGWO, MPA, and BPSO, respectively whereas, the SGA and WOA showed the worst results. One of these datasets has both clinical and image data. Comparison with other previous works using accuracy measure. Li et al.36 proposed an FS method using a discrete artificial bee colony (ABC) to improve the classification of Parkinsons disease. One of the main disadvantages of our approach is that its built basically within two different environments. Brain tumor segmentation with deep neural networks. Covid-19 dataset. Design incremental data augmentation strategy for COVID-19 CT data. 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. In this paper, we try to integrate deep transfer-learning-based methods, along with a convolutional block attention module (CBAM), to focus on the relevant portion of the feature maps to conduct an image-based classification of human monkeypox disease. The algorithm combines the assessment of image quality, digital image processing and deep learning for segmentation of the lung tissues and their classification. 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). Isolation and characterization of a bat sars-like coronavirus that uses the ace2 receptor. kharrat and Mahmoud32proposed an FS method based on a hybrid of Simulated Annealing (SA) and GA to classify brain tumors using MRI. However, WOA showed the worst performances in these measures; which means that if it is run in the same conditions several times, the same results will be obtained. Automatic COVID-19 lung images classification system based on convolution neural network. implemented the deep neural networks and classification as well as prepared the related figures and manuscript text. Access through your institution. (14)-(15) are implemented in the first half of the agents that represent the exploitation. 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). He, K., Zhang, X., Ren, S. & Sun, J. 4b, FO-MPA algorithm selected successfully fewer features than other algorithms, as it selected 130 and 86 features from Dataset 1 and Dataset 2, respectively. In this paper, different Conv. Narayanan, S.J., Soundrapandiyan, R., Perumal, B. The given Kaggle dataset consists of chest CT scan images of patients suffering from the novel COVID-19, other pulmonary disorders, and those of healthy patients. FCM reinforces the ANFIS classification learning phase based on the features of COVID-19 patients. Robertas Damasevicius. & Wang, W. Medical image segmentation using fruit fly optimization and density peaks clustering. The largest features were selected by SMA and SGA, respectively. The results show that, using only 6 epochs for training, the CNNs achieved very high performance on the classification task. 79, 18839 (2020). Jcs: An explainable covid-19 diagnosis system by joint classification and segmentation. There are three main parameters for pooling, Filter size, Stride, and Max pool. 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). Faramarzi, A., Heidarinejad, M., Mirjalili, S. & Gandomi, A. H. Marine predators algorithm: a nature-inspired metaheuristic. 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. For the image features that led to categorization, there were varied levels of agreement in the interobserver and intraobserver components that . Metric learning Metric learning can create a space in which image features within the. Also, As seen in Fig. Methods Med. Therefore, in this paper, we propose a hybrid classification approach of COVID-19. The test accuracy obtained for the model was 98%. Fractional Differential Equations: An Introduction to Fractional Derivatives, Fdifferential Equations, to Methods of their Solution and Some of Their Applications Vol. 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: Etymology. Image Underst. Based on Standard Deviation measure (STD), the most stable algorithms were SCA, SGA, BPSO, and bGWO, respectively. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. Moreover, a multi-objective genetic algorithm was applied to search for the optimal features subset. In my thesis project, I developed an image classification model to detect COVID-19 on chest X-ray medical data using deep learning models such . 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. Faramarzi et al.37 implement this feature via saving the previous best solutions of a prior iteration, and compared with the current ones; the solutions are modified based on the best one during the comparison stage. what medical images are commonly used for COVID-19 classification and what are the methods for COVID-19 classification. In Iberian Conference on Pattern Recognition and Image Analysis, 176183 (Springer, 2011). Al-qaness, M. A., Ewees, A. Harikumar, R. & Vinoth Kumar, B. D.Y. As a result, the obtained outcomes outperformed previous works in terms of the models general performance measure. Hence, the FC memory is applied during updating the prey locating in the second step of the algorithm to enhance the exploitation stage. However, it has some limitations that affect its quality. This dataset currently contains hundreds of frontal view X-rays and is the largest public resource for COVID-19 image and prognostic data, making it a necessary resource to develop and evaluate tools to aid in the treatment of CO VID-19. They were also collected frontal and lateral view imagery and metadata such as the time since first symptoms, intensive care unit (ICU) status, survival status, intubation status, or hospital location. Layers are applied to extract different types of features such as edges, texture, colors, and high-lighted patterns from the images. 101, 646667 (2019). The model was developed using Keras library47 with Tensorflow backend48. 152, 113377 (2020). Recombinant: A process in which the genomes of two SARS-CoV-2 variants (that have infected a person at the same time) combine during the viral replication process to form a new variant that is different . PVT-COV19D: COVID-19 Detection Through Medical Image Classification Based on Pyramid Vision Transformer. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 19 (2015). wrote the intro, related works and prepare results. In this work, we have used four transfer learning models, VGG16, InceptionV3, ResNet50, and DenseNet121 for the classification tasks. COVID-19 tests are currently hard to come by there are simply not enough of them and they cannot be manufactured fast enough, which is causing panic. However, some of the extracted features by CNN might not be sufficient, which may affect negatively the quality of the classification images. Wu, Y.-H. etal. Li, H. etal. Then, applying the FO-MPA to select the relevant features from the images. Marine memory: This is the main feature of the marine predators and it helps in catching the optimal solution very fast and avoid local solutions. Google Scholar. In57, ResNet-50 CNN has been applied after applying horizontal flipping, random rotation, random zooming, random lighting, and random wrapping on raw images. They concluded that the hybrid method outperformed original fuzzy c-means, and it had less sensitive to noises. 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. Dhanachandra, N. & Chanu, Y. J. 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. The Softmax activation function is used for this purpose because the output should be binary (positive COVID-19 negative COVID-19). Finally, the predator follows the levy flight distribution to exploit its prey location. 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 a swarm-based feature selection algorithm (Marine Predators Algorithm) to select the most relevant features. Automatic CNN-based Chest X-Ray (CXR) image classification for detecting Covid-19 attracted so much attention. 22, 573577 (2014). (5). 43, 635 (2020). International Conference on Machine Learning647655 (2014). Radiology 295, 2223 (2020). ADS arXiv preprint arXiv:2003.13815 (2020). Credit: NIAID-RML CAS The proposed IFM approach is summarized as follows: Extracting deep features from Inception, where about 51 K features were extracted. Dual feature selection and rebalancing strategy using metaheuristic optimization algorithms in x-ray image datasets. Zhang et al.16 proposed a kernel feature selection method to segment brain tumors from MRI images. 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.

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