covid 19 image classification

COVID-19 image classification using deep features and fractional-order marine predators algorithm. Table2 depicts the variation in morphology of the image, lighting, structure, black spaces, shape, and zoom level among the same dataset, as well as with the other dataset. 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. Deep cnns for microscopic image classification by exploiting transfer learning and feature concatenation. & Mirjalili, S. Slime mould algorithm: A new method for stochastic optimization. In Iberian Conference on Pattern Recognition and Image Analysis, 176183 (Springer, 2011). arXiv preprint arXiv:1711.05225 (2017). Arijit Dey, Soham Chattopadhyay, Ram Sarkar, Dandi Yang, Cristhian Martinez, Jesus Carretero, Jess Alejandro Alzate-Grisales, Alejandro Mora-Rubio, Reinel Tabares-Soto, Lo Dumortier, Florent Gupin, Thomas Grenier, Linda Wang, Zhong Qiu Lin & Alexander Wong, Afnan Al-ali, Omar Elharrouss, Somaya Al-Maaddeed, Robbie Sadre, Baskaran Sundaram, Daniela Ushizima, Zahid Ullah, Muhammad Usman, Jeonghwan Gwak, Scientific Reports arXiv preprint arXiv:2004.05717 (2020). 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. Radiomics: extracting more information from medical images using advanced feature analysis. 25, 3340 (2015). 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. (14)(15) to emulate the motion of the first half of the population (prey) and Eqs. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition12511258 (2017). (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. Syst. Google Scholar. Therefore, several pre-trained models have won many international image classification competitions such as VGGNet24, Resnet25, Nasnet26, Mobilenet27, Inception28 and Xception29. Phys. In ancient India, according to Aelian, it was . The predator tries to catch the prey while the prey exploits the locations of its food. Finally, the predator follows the levy flight distribution to exploit its prey location. Negative COVID-19 images were collected from another Chest X-ray Kaggle published dataset43. (2) calculated two child nodes. Finally, the sum of the features importance value on each tree is calculated then divided by the total number of trees as in Eq. Li, S., Chen, H., Wang, M., Heidari, A. Vis. Access through your institution. where \(REfi_{i}\) represents the importance of feature i that were calculated from all trees, where \(normfi_{ij}\) is the normalized feature importance for feature i in tree j, also T is the total number of trees. 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. In this work, we have used four transfer learning models, VGG16, InceptionV3, ResNet50, and DenseNet121 for the classification tasks. They were manually aggregated from various web based repositories into a machine learning (ML) friendly format with accompanying data loader code. In Smart Intelligent Computing and Applications, 305313 (Springer, 2019). Math. Comput. 4 and Table4 list these results for all algorithms. FCM reinforces the ANFIS classification learning phase based on the features of COVID-19 patients. 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. Alhamdulillah, glad to share that our paper entitled "Multi-class classification of brain tumor types from MR Images using EfficientNets" has been accepted for Sign up for the Nature Briefing newsletter what matters in science, free to your inbox daily. It also contributes to minimizing resource consumption which consequently, reduces the processing time. Both the model uses Lungs CT Scan images to classify the covid-19. 121, 103792 (2020). Ozturk, T. et al. Currently, we witness the severe spread of the pandemic of the new Corona virus, COVID-19, which causes dangerous symptoms to humans and animals, its complications may lead to death. Ge, X.-Y. Average of the consuming time and the number of selected features in both datasets. Bisong, E. Building Machine Learning and Deep Learning Models on Google Cloud Platform (Springer, Berlin, 2019). Scientific Reports (Sci Rep) In this paper, filters of size 2, besides a stride of 2 and \(2 \times 2\) as Max pool, were adopted. Abadi, M. et al. Credit: NIAID-RML Can ai help in screening viral and covid-19 pneumonia? Computed tomography (CT) and magnetic resonance imaging (MRI) represent valuable input to AI algorithms, scanning human body sections for the sake of diagnosis. where \(R\in [0,1]\) is a random vector drawn from a uniform distribution and \(P=0.5\) is a constant number. The \(\delta\) symbol refers to the derivative order coefficient. Figure5 illustrates the convergence curves for FO-MPA and other algorithms in both datasets. The largest features were selected by SMA and SGA, respectively. layers is to extract features from input images. While the second dataset, dataset 2 was collected by a team of researchers from Qatar University in Qatar and the University of Dhaka in Bangladesh along with collaborators from Pakistan and Malaysia medical doctors44. Health Inf. Book Methods Med. Ozturk et al. Performance analysis of neural networks for classification of medical images with wavelets as a feature extractor. Therefore, in this paper, we propose a hybrid classification approach of COVID-19. https://keras.io (2015). Keywords - Journal. Future Gener. In Table4, for Dataset 1, the proposed FO-MPA approach achieved the highest accuracy in the best and mean measures, as it reached 98.7%, and 97.2% of correctly classified samples, respectively. The focus of this study is to evaluate and examine a set of deep learning transfer learning techniques applied to chest radiograph images for the classification of COVID-19, normal (healthy), and pneumonia. Comput. 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 . Two real datasets about COVID-19 patients are studied in this paper. The two datasets consist of X-ray COVID-19 images by international Cardiothoracic radiologist, researchers and others published on Kaggle. 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). Stage 1: After the initialization, the exploration phase is implemented to discover the search space. (3), the importance of each feature is then calculated. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. The symbol \(r\in [0,1]\) represents a random number. In some cases (as exists in this work), the dataset is limited, so it is not sufficient for building & training a CNN. Convolutional neural networks were implemented in Python 3 under Google Colaboratory46, commonly referred to as Google Colab, which is a research project for prototyping machine learning models on powerful hardware options such as GPUs and TPUs. 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 . Zhu, H., He, H., Xu, J., Fang, Q. The first one is based on Python, where the deep neural network architecture (Inception) was built and the feature extraction part was performed. In Future of Information and Communication Conference, 604620 (Springer, 2020). Use the Previous and Next buttons to navigate the slides or the slide controller buttons at the end to navigate through each slide. One of the main disadvantages of our approach is that its built basically within two different environments. In 2019 26th National and 4th International Iranian Conference on Biomedical Engineering (ICBME), 194198 (IEEE, 2019). Highlights COVID-19 CT classification using chest tomography (CT) images. 6 (left), for dataset 1, it can be seen that our proposed FO-MPA approach outperforms other CNN models like VGGNet, Xception, Inception, Mobilenet, Nasnet, and Resnet. (18)(19) for the second half (predator) as represented below. Al-qaness, M. A., Ewees, A. 4a, the SMA was considered as the fastest algorithm among all algorithms followed by BPSO, FO-MPA, and HHO, respectively, while MPA was the slowest algorithm. Wish you all a very happy new year ! For the special case of \(\delta = 1\), the definition of Eq. Yousri, D. & Mirjalili, S. Fractional-order cuckoo search algorithm for parameter identification of the fractional-order chaotic, chaotic with noise and hyper-chaotic financial systems. 69, 4661 (2014). 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). Hence, the FC memory is applied during updating the prey locating in the second step of the algorithm to enhance the exploitation stage. The whale optimization algorithm. While, MPA, BPSO, SCA, and SGA obtained almost the same accuracy, followed by both bGWO, WOA, and SMA. & 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. 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. Based on54, the later step reduces the memory requirements, and improve the efficiency of the framework. Multimedia Tools Appl. 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 Diagnosis of parkinsons disease with a hybrid feature selection algorithm based on a discrete artificial bee colony. 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/]. While no feature selection was applied to select best features or to reduce model complexity. Google Scholar. Machine learning (ML) methods can play vital roles in identifying COVID-19 patients by visually analyzing their chest x-ray images. Netw. MATH Harikumar, R. & Vinoth Kumar, B. One from the well-know definitions of FC is the Grunwald-Letnikov (GL), which can be mathematically formulated as below40: where \(D^{\delta }(U(t))\) refers to the GL fractional derivative of order \(\delta\). Use of chest ct in combination with negative rt-pcr assay for the 2019 novel coronavirus but high clinical suspicion. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 19 (2015). The shape of the output from the Inception is (5, 5, 2048), which represents a feature vector of size 51200. Tree based classifier are the most popular method to calculate feature importance to improve the classification since they have high accuracy, robustness, and simple38. Aiming at the problems of poor attention to existing translation models, the insufficient ability of key transfer and generation, insufficient quality of generated images, and lack of detailed features, this paper conducts research on lung medical image translation and lung image classification based on . Cancer 48, 441446 (2012). Cauchemez, S. et al. Afzali, A., Mofrad, F.B. From Fig. They applied the SVM classifier with and without RDFS. Refresh the page, check Medium 's site status, or find something interesting. Coronavirus Disease (COVID-19): A primer for emergency physicians (2020) Summer Chavez et al. M.A.E. 2 (left). (14)-(15) are implemented in the first half of the agents that represent the exploitation. This stage can be mathematically implemented as below: In Eq. J. They used K-Nearest Neighbor (kNN) to classify x-ray images collected from Montgomery dataset, and it showed good performances. Imaging 35, 144157 (2015). 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. 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. 95, 5167 (2016). \(Fit_i\) denotes a fitness function value. Biol. Stage 2: The prey/predator in this stage begin exploiting the best location that detects for their foods. and M.A.A.A. 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 addition, the good results achieved by the FO-MPA against other algorithms can be seen as an advantage of FO-MPA, where a balancing between exploration and exploitation stages and escaping from local optima were achieved. Then the best solutions are reached which determine the optimal/relevant features that should be used to address the desired output via several performance measures. Automatic COVID-19 lung images classification system based on convolution neural network. Key Definitions. 10, 10331039 (2020). In this subsection, the results of FO-MPA are compared against most popular and recent feature selection algorithms, such as Whale Optimization Algorithm (WOA)49, Henry Gas Solubility optimization (HGSO)50, Sine cosine Algorithm (SCA), Slime Mould Algorithm (SMA)51, Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO)52, Harris Hawks Optimization (HHO)53, Genetic Algorithm (GA), and basic MPA. Support Syst. Johnson, D.S., Johnson, D. L.L., Elavarasan, P. & Karunanithi, A. Memory FC prospective concept (left) and weibull distribution (right). Classification of COVID19 using Chest X-ray Images in Keras 4.6 33 ratings Share Offered By In this Guided Project, you will: Learn to Build and Train the Convolutional Neural Network using Keras with Tensorflow as Backend Learn to Visualize Data in Matplotlib Learn to make use of the Trained Model to Predict on a New Set of Data 2 hours COVID-19 Chest X -Ray Image Classification with Neural Network Currently we are suffering from COVID-19, and the situation is very serious. Extensive evaluation experiments had been carried out with a collection of two public X-ray images datasets. I am passionate about leveraging the power of data to solve real-world problems. The following stage was to apply Delta variants. Biocybern. It is also noted that both datasets contain a small number of positive COVID-19 images, and up to our knowledge, there is no other sufficient available published dataset for COVID-19. It is important to detect positive cases early to prevent further spread of the outbreak. 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. 41, 923 (2019). Also, because COVID-19 is a virus, distinguish COVID-19 from common viral . Table3 shows the numerical results of the feature selection phase for both datasets. The optimum path forest (OPF) classifier was applied to classify pulmonary nodules based on CT images. 2020-09-21 . 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. The whole dataset contains around 200 COVID-19 positive images and 1675 negative COVID19 images. The second CNN architecture classifies the X-ray image into three classes, i.e., normal, pneumonia, and COVID-19. Purpose The study aimed at developing an AI . Appl. Rajpurkar, P. etal. (5). Its structure is designed based on experts' knowledge and real medical process. . 0.9875 and 0.9961 under binary and multi class classifications respectively. (20), \(FAD=0.2\), and W is a binary solution (0 or 1) that corresponded to random solutions. Intell. Acharya et al.11 applied different FS methods to classify Alzheimers disease using MRI images. As a result, the obtained outcomes outperformed previous works in terms of the models general performance measure. contributed to preparing results and the final figures. According to the promising results of the proposed model, that combines CNN as a feature extractor and FO-MPA as a feature selector could be useful and might be successful in being applied in other image classification tasks. J. Med. PubMed Central arXiv preprint arXiv:1704.04861 (2017). The evaluation showed that the RDFS improved SVM robustness against reconstruction kernel and slice thickness. Automatic CNN-based Chest X-Ray (CXR) image classification for detecting Covid-19 attracted so much attention. The MCA-based model is used to process decomposed images for further classification with efficient storage. The combination of Conv. Tensorflow: Large-scale machine learning on heterogeneous systems, 2015. Mirjalili, S. & Lewis, A. My education and internships have equipped me with strong technical skills in Python, deep learning models, machine learning classification, text classification, and more. Adv. However, the proposed IMF approach achieved the best results among the compared algorithms in least time. Get the most important science stories of the day, free in your inbox. 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. It based on using a deep convolutional neural network (Inception) for extracting features from COVID-19 images, then filtering the resulting features using Marine Predators Algorithm (MPA), enhanced by fractional-order calculus(FO). Evaluation outcomes showed that GA based FS methods outperformed traditional approaches, such as filter based FS and traditional wrapper methods. Int. Li et al.34 proposed a self-adaptive bat algorithm (BA) to address two problems in lung X-ray images, rebalancing, and feature selection. Table2 shows some samples from two datasets. Also, it has killed more than 376,000 (up to 2 June 2020) [Coronavirus disease (COVID-2019) situation reports: (https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports/)]. the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in The proposed COVID-19 X-ray classification approach starts by applying a CNN (especially, a powerful architecture called Inception which pre-trained on Imagnet dataset) to extract the discriminant features from raw images (with no pre-processing or segmentation) from the dataset that contains positive and negative COVID-19 images. This algorithm is tested over a global optimization problem. Correspondence to 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. & Pouladian, M. Feature selection for contour-based tuberculosis detection from chest x-ray images. Also, in12, an Fs method based on SVM was proposed to detect Alzheimers disease from SPECT images. Eng. Also, some image transformations were applied, such as rotation, horizontal flip, and scaling. Such methods might play a significant role as a computer-aided tool for image-based clinical diagnosis soon. Stage 3: This stage executed on the last third of the iteration numbers (\(t>\frac{2}{3}t_{max}\)) where based on the following formula: Eddy formation and Fish Aggregating Devices effect: Faramarzi et al.37 considered the external impacts from the environment, such as the eddy formation or Fish Aggregating Devices (FADs) effects to avoid the local optimum solutions. The test accuracy obtained for the model was 98%. They employed partial differential equations for extracting texture features of medical images. However, it was clear that VGG19 and MobileNet achieved the best performance over other CNNs. SharifRazavian, A., Azizpour, H., Sullivan, J. A survey on deep learning in medical image analysis. Feature selection based on gaussian mixture model clustering for the classification of pulmonary nodules based on computed tomography. In Dataset 2, FO-MPA also is reported as the highest classification accuracy with the best and mean measures followed by the BPSO. For Dataset 2, FO-MPA showed acceptable (not the best) performance, as it achieved slightly similar results to the first and second ranked algorithm (i.e., MPA and SMA) on mean, best, max, and STD measures. Lilang Zheng, Jiaxuan Fang, Xiaorun Tang, Hanzhang Li, Jiaxin Fan, Tianyi Wang, Rui Zhou, Zhaoyan Yan. Article 2 (right). The second one is based on Matlab, where the feature selection part (FO-MPA algorithm) was performed. Szegedy, C. et al. E. B., Traina-Jr, C. & Traina, A. J. 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). Google Scholar. Li, H. etal. Heidari, A. 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. Also, As seen in Fig. (1): where \(O_k\) and \(E_k\) refer to the actual and the expected feature value, respectively. (33)), showed that FO-MPA also achieved the best value of the fitness function compared to others. You are using a browser version with limited support for CSS. COVID 19 X-ray image classification. To further analyze the proposed algorithm, we evaluate the selected features by FO-MPA by performing classification. Harris hawks optimization: algorithm and applications. Lambin, P. et al. In14, the authors proposed an FS method based on a convolutional neural network (CNN) to detect pneumonia from lung X-ray images. The memory terms of the prey are updated at the end of each iteration based on first in first out concept. To survey the hypothesis accuracy of the models. With the help of numerous algorithms in AI, modern COVID-19 cases can be detected and managed in a classified framework. 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. A. The results showed that the proposed approach showed better performances in both classification accuracy and the number of extracted features that positively affect resource consumption and storage efficiency. implemented the deep neural networks and classification as well as prepared the related figures and manuscript text. Comput. Havaei, M. et al. There are three main parameters for pooling, Filter size, Stride, and Max pool. Generally, the most stable algorithms On dataset 1 are WOA, SCA, HGSO, FO-MPA, and SGA, respectively. ADS Med. The classification accuracy of MPA, WOA, SCA, and SGA are almost the same. 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. arXiv preprint arXiv:2003.11597 (2020). \(\Gamma (t)\) indicates gamma function. Going deeper with convolutions. Accordingly, that reflects on efficient usage of memory, and less resource consumption. Detection of lung cancer on chest ct images using minimum redundancy maximum relevance feature selection method with convolutional neural networks. (22) can be written as follows: By using the discrete form of GL definition of Eq. & Cmert, Z. As seen in Fig. 1. First: prey motion based on FC the motion of the prey of Eq. Google Scholar. Medical imaging techniques are very important for diagnosing diseases. Adv. The Marine Predators Algorithm (MPA)is a recently developed meta-heuristic algorithm that emulates the relation among the prey and predator in nature37. COVID-19 image classification using deep learning: Advances, challenges and opportunities COVID-19 image classification using deep learning: Advances, challenges and opportunities Comput Biol Med. PubMed 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 . Eng. Inception architecture is described in Fig. Wu, Y.-H. etal. Google Scholar. \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. Howard, A.G. etal. Accordingly, the FC is an efficient tool for enhancing the performance of the meta-heuristic algorithms by considering the memory perspective during updating the solutions. An image segmentation approach based on fuzzy c-means and dynamic particle swarm optimization algorithm. MathSciNet In9, to classify ultrasound medical images, the authors used distance-based FS methods and a Fuzzy Support Vector Machine (FSVM). Narayanan, S.J., Soundrapandiyan, R., Perumal, B. We are hiring! It also shows that FO-MPA can select the smallest subset of features, which reflects positively on performance. volume10, Articlenumber:15364 (2020) One of the best methods of detecting. 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. For each decision tree, node importance is calculated using Gini importance, Eq. CAS & Cmert, Z. However, using medical imaging, chest CT, and chest X-ray scan can play a critical role in COVID-19 diagnosis. The prey follows Weibull distribution during discovering the search space to detect potential locations of its food. 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. Comput. These images have been further used for the classification of COVID-19 and non-COVID-19 images using ResNet50 and AlexNet convolutional neural network (CNN) models. Moreover, we design a weighted supervised loss that assigns higher weight for . They applied a fuzzy decision tree classifier, and they found that fuzzy PSO improved the classification accuracy. Civit-Masot et al. The Softmax activation function is used for this purpose because the output should be binary (positive COVID-19 negative COVID-19). Background The large volume and suboptimal image quality of portable chest X-rays (CXRs) as a result of the COVID-19 pandemic could post significant challenges for radiologists and frontline physicians.

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