In Proceedings of the IEEE Conference on computer vision and pattern recognition workshops, 806813 (2014). Authors On the second dataset, dataset 2 (Fig. Mutation: A mutation refers to a single change in a virus's genome (genetic code).Mutations happen frequently, but only sometimes change the characteristics of the virus. 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. 51, 810820 (2011). Computational image analysis techniques play a vital role in disease treatment and diagnosis. Johnson, D.S., Johnson, D. L.L., Elavarasan, P. & Karunanithi, A. 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 . Imaging 35, 144157 (2015). Introduction They concluded that the hybrid method outperformed original fuzzy c-means, and it had less sensitive to noises. . Table4 show classification accuracy of FO-MPA compared to other feature selection algorithms, where the best, mean, and STD for classification accuracy were calculated for each one, besides time consumption and the number of selected features (SF). Google Scholar. Also, in12, an Fs method based on SVM was proposed to detect Alzheimers disease from SPECT images. Automatic CNN-based Chest X-Ray (CXR) image classification for detecting Covid-19 attracted so much attention. The proposed IFM approach is summarized as follows: Extracting deep features from Inception, where about 51 K features were extracted. used VGG16 to classify Covid-19 and achieved good results with an accuracy of 86% [ 22 ]. Deep learning plays an important role in COVID-19 images diagnosis. Figure5 illustrates the convergence curves for FO-MPA and other algorithms in both datasets. Med. Deep residual learning for image recognition. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. 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). Classification of COVID-19 X-ray images with Keras and its potential problem | by Yiwen Lai | Analytics Vidhya | Medium Write Sign up 500 Apologies, but something went wrong on our end.. Biomed. COVID-19 image classification using deep features and fractional-order marine predators algorithm Authors. Syst. Duan, H. et al. 43, 635 (2020). Sohail, A. S.M., Bhattacharya, P., Mudur, S.P. & Krishnamurthy, S. Classification of ultrasound medical images using distance based feature selection and fuzzy-svm. and pool layers, three fully connected layers, the last one performs classification. (4). & Zhu, Y. Kernel feature selection to fuse multi-spectral mri images for brain tumor segmentation. 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. Li, S., Chen, H., Wang, M., Heidari, A. 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 \(\delta\). Knowl. Havaei, M. et al. where r is the run numbers. It also shows that FO-MPA can select the smallest subset of features, which reflects positively on performance. & Dai, Q. Discriminative clustering and feature selection for brain mri segmentation. In this paper, different Conv. To segment brain tissues from MRI images, Kong et al.17 proposed an FS method using two methods, called a discriminative clustering method and the information theoretic discriminative segmentation. 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]. The symbol \(r\in [0,1]\) represents a random number. While55 used different CNN structures. Biol. Methods Med. 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. Lambin, P. et al. Syst. Two real datasets about COVID-19 patients are studied in this paper. From Fig. https://doi.org/10.1016/j.future.2020.03.055 (2020). Harikumar, R. & Vinoth Kumar, B. For the special case of \(\delta = 1\), the definition of Eq. Toaar, M., Ergen, B. The convergence behaviour of FO-MPA was evaluated over 25 independent runs and compared to other algorithms, where the x-axis and the y-axis represent the iterations and the fitness value, respectively. 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 proposed IMF approach is employed to select only relevant and eliminate unnecessary features. After applying this technique, the feature vector is minimized from 2000 to 459 and from 2000 to 462 for Dataset1 and Dataset 2, respectively. They compared the BA to PSO, and the comparison outcomes showed that BA had better performance. Softw. The data was collected mainly from retrospective cohorts of pediatric patients from Guangzhou Women and Childrens medical center. https://www.sirm.org/category/senza-categoria/covid-19/ (2020). The MCA-based model is used to process decomposed images for further classification with efficient storage. More so, a combination of partial differential equations and deep learning was applied for medical image classification by10. \end{aligned} \end{aligned}$$, $$\begin{aligned} \begin{aligned} U_{i}(t+1)&= \frac{1}{1!} Syst. The main purpose of Conv. Simonyan, K. & Zisserman, A. 97, 849872 (2019). M.A.E. In 2019 26th National and 4th International Iranian Conference on Biomedical Engineering (ICBME), 194198 (IEEE, 2019). arXiv preprint arXiv:2003.13145 (2020). 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. Heidari, A. Our method is able to classify pneumonia from COVID-19 and visualize an abnormal area at the same time. 198 (Elsevier, Amsterdam, 1998). Also, because COVID-19 is a virus, distinguish COVID-19 from common viral . Comput. Tree based classifier are the most popular method to calculate feature importance to improve the classification since they have high accuracy, robustness, and simple38. The results are the best achieved on these datasets when compared to a set of recent feature selection algorithms. 22, 573577 (2014). The memory properties of Fc calculus makes it applicable to the fields that required non-locality and memory effect. Whereas, the worst algorithm was BPSO. In this experiment, the selected features by FO-MPA were classified using KNN. Bukhari, S. U.K., Bukhari, S. S.K., Syed, A. Google Scholar. Furthermore, deep learning using CNN is considered one of the best choices in medical imaging applications20, especially classification. Comparison with other previous works using accuracy measure. 2. Also, some image transformations were applied, such as rotation, horizontal flip, and scaling. Access through your institution. Med. All classication models ever, the virus mutates, and new variants emerge and dis- performed better in classifying the Non-COVID-19 images appear. 43, 302 (2019). layers is to extract features from input images. Whereas, FO-MPA, MPA, HGSO, and WOA showed similar STD results. Automatic COVID-19 lung images classification system based on convolution neural network. Johnson et al.31 applied the flower pollination algorithm (FPA) to select features from CT images of the lung, to detect lung cancers. If the random solution is less than 0.2, it converted to 0 while the random solution becomes 1 when the solutions are greater than 0.2. Accordingly, the prey position is upgraded based the following equations. Sci. Etymology. J. Clin. what medical images are commonly used for COVID-19 classification and what are the methods for COVID-19 classification. 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. An image segmentation approach based on fuzzy c-means and dynamic particle swarm optimization algorithm. 101, 646667 (2019). Metric learning Metric learning can create a space in which image features within the. Chollet, F. Xception: Deep learning with depthwise separable convolutions. You have a passion for computer science and you are driven to make a difference in the research community? The classification accuracy of MPA, WOA, SCA, and SGA are almost the same. One of these datasets has both clinical and image data. They also used the SVM to classify lung CT images. Nguyen, L.D., Lin, D., Lin, Z. Inspired by this concept, Faramarzi et al.37 developed the MPA algorithm by considering both of a predator a prey as solutions. where CF is the parameter that controls the step size of movement for the predator. The model was developed using Keras library47 with Tensorflow backend48. Lilang Zheng, Jiaxuan Fang, Xiaorun Tang, Hanzhang Li, Jiaxin Fan, Tianyi Wang, Rui Zhou, Zhaoyan Yan. SharifRazavian, A., Azizpour, H., Sullivan, J. MATH The results show that, using only 6 epochs for training, the CNNs achieved very high performance on the classification task. 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). Generally, the proposed FO-MPA approach showed satisfying performance in both the feature selection ratio and the classification rate. 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 . As seen in Fig. For this motivation, we utilize the FC concept with the MPA algorithm to boost the second step of the standard version of the algorithm. It can be concluded that FS methods have proven their advantages in different medical imaging applications19. ADS chest X-ray images into three classes of COVID-19, normal chest X-ray and other lung diseases. 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/]. Faramarzi, A., Heidarinejad, M., Mirjalili, S. & Gandomi, A. H. Marine predators algorithm: a nature-inspired metaheuristic. Also, As seen in Fig. In the current work, the values of k, and \(\zeta\) are set to 2, and 2, respectively. 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 paper, after applying Chi-square, the feature vector is minimized for both datasets from 51200 to 2000. implemented the deep neural networks and classification as well as prepared the related figures and manuscript text. 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. The second CNN architecture classifies the X-ray image into three classes, i.e., normal, pneumonia, and COVID-19. The next process is to compute the performance of each solution using fitness value and determine which one is the best solution. the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in https://doi.org/10.1038/s41598-020-71294-2, DOI: https://doi.org/10.1038/s41598-020-71294-2. Transmission scenarios for middle east respiratory syndrome coronavirus (mers-cov) and how to tell them apart. Extensive comparisons had been implemented to compare the FO-MPA with several feature selection algorithms, including SMA, HHO, HGSO, WOA, SCA, bGWO, SGA, BPSO, besides the classic MPA. \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. Feature selection based on gaussian mixture model clustering for the classification of pulmonary nodules based on computed tomography. Memory FC prospective concept (left) and weibull distribution (right). 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. The HGSO also was ranked last. Epub 2022 Mar 3. 25, 3340 (2015). Negative COVID-19 images were collected from another Chest X-ray Kaggle published dataset43. Extensive evaluation experiments had been carried out with a collection of two public X-ray images datasets. 9, 674 (2020). In our example the possible classifications are covid, normal and pneumonia. In57, ResNet-50 CNN has been applied after applying horizontal flipping, random rotation, random zooming, random lighting, and random wrapping on raw images. }, \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. 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. COVID 19 X-ray image classification. & Cmert, Z. Performance analysis of neural networks for classification of medical images with wavelets as a feature extractor. Automated detection of covid-19 cases using deep neural networks with x-ray images. In transfer learning, a CNN which was previously trained on a large & diverse image dataset can be applied to perform a specific classification task by23. 40, 2339 (2020). 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. (iii) To implement machine learning classifiers for classification of COVID and non-COVID image classes. Narayanan, S.J., Soundrapandiyan, R., Perumal, B. Fractional Differential Equations: An Introduction to Fractional Derivatives, Fdifferential Equations, to Methods of their Solution and Some of Their Applications Vol. Moreover, a multi-objective genetic algorithm was applied to search for the optimal features subset. The whale optimization algorithm. Using X-ray images we can train a machine learning classifier to detect COVID-19 using Keras and TensorFlow. 95, 5167 (2016). The Weibull Distribution is a heavy-tied distribution which presented as in Fig. In Eq. Meanwhile, the prey moves effectively based on its memory for the previous events to catch its food, as presented in Eq. To obtain Four measures for the proposed method and the compared algorithms are listed. Appl. Correspondence to A survey on deep learning in medical image analysis. PubMed In this paper, Inception is applied as a feature extractor, where the input image shape is (229, 229, 3). 132, 8198 (2018). 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. Future Gener. They applied the SVM classifier for new MRI images to segment brain tumors, automatically. ), such as \(5\times 5\), \(3 \times 3\), \(1 \times 1\). Inception architecture is described in Fig. 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 . So, for a \(4 \times 4\) matrix, will result in \(2 \times 2\) matrix after applying max pooling. Google Scholar. 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. Tensorflow: Large-scale machine learning on heterogeneous systems, 2015. (18)(19) for the second half (predator) as represented below. FCM reinforces the ANFIS classification learning phase based on the features of COVID-19 patients. First: prey motion based on FC the motion of the prey of Eq. In Future of Information and Communication Conference, 604620 (Springer, 2020). Classification Covid-19 X-Ray Images | by Falah Gatea | Medium 500 Apologies, but something went wrong on our end. 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. 111, 300323. 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. Such methods might play a significant role as a computer-aided tool for image-based clinical diagnosis soon. 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 . A., Fan, H. & Abd ElAziz, M. Optimization method for forecasting confirmed cases of covid-19 in china. For general case based on the FC definition, the Eq. For both datasets, the Covid19 images were collected from patients with ages ranging from 40-84 from both genders. 11314, 113142S (International Society for Optics and Photonics, 2020). Improving the ranking quality of medical image retrieval using a genetic feature selection method. & Baby, C.J. Emphysema medical image classification using fuzzy decision tree with fuzzy particle swarm optimization clustering. AMERICAN JOURNAL OF EMERGENCY MEDICINE COVID-19: Facemask use prevalence in international airports in Asia, Europe and the Americas, March 2020 Cohen, J.P., Morrison, P. & Dao, L. Covid-19 image data collection. Comput. Future Gener. Litjens, G. et al. J. Med. Pool layers are used mainly to reduce the inputs size, which accelerates the computation as well. Eng. In Iberian Conference on Pattern Recognition and Image Analysis, 176183 (Springer, 2011). Highlights COVID-19 CT classification using chest tomography (CT) images. (20), \(FAD=0.2\), and W is a binary solution (0 or 1) that corresponded to random solutions. Generally, the most stable algorithms On dataset 1 are WOA, SCA, HGSO, FO-MPA, and SGA, respectively. Multimedia Tools Appl. PVT-COV19D: COVID-19 Detection Through Medical Image Classification Based on Pyramid Vision Transformer. I am passionate about leveraging the power of data to solve real-world problems. Scientific Reports (Sci Rep) One of the main disadvantages of our approach is that its built basically within two different environments. Sci. Cite this article. 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. They are distributed among people, bats, mice, birds, livestock, and other animals1,2. Med. \(Fit_i\) denotes a fitness function value. Li, J. et al. Dual feature selection and rebalancing strategy using metaheuristic optimization algorithms in x-ray image datasets. 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 . Sahlol, A.T., Yousri, D., Ewees, A.A. et al. While no feature selection was applied to select best features or to reduce model complexity. 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. \(r_1\) and \(r_2\) are the random index of the prey. Computed tomography (CT) and magnetic resonance imaging (MRI) represent valuable input to AI algorithms, scanning human body sections for the sake of diagnosis. 115, 256269 (2011). 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. (8) can be remodeled as below: where \(D^1[x(t)]\) represents the difference between the two followed events. 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. (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. ADS arXiv preprint arXiv:2003.11597 (2020). My education and internships have equipped me with strong technical skills in Python, deep learning models, machine learning classification, text classification, and more. (3), the importance of each feature is then calculated. Stage 1: After the initialization, the exploration phase is implemented to discover the search space. They applied a fuzzy decision tree classifier, and they found that fuzzy PSO improved the classification accuracy. The 30-volume set, comprising the LNCS books 12346 until 12375, constitutes the refereed proceedings of the 16th European Conference on Computer Vision, ECCV 2020, which was planned to be held in Glasgow, UK, during August 23-28, 2020. and JavaScript. 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. On January 20, 2023, Japanese Prime Minister Fumio Kishida announced that the country would be downgrading the COVID-19 classification. Brain tumor segmentation with deep neural networks. J. Inf. In this subsection, a comparison with relevant works is discussed. \(\bigotimes\) indicates the process of element-wise multiplications. The main contributions of this study are elaborated as follows: Propose an efficient hybrid classification approach for COVID-19 using a combination of CNN and an improved swarm-based feature selection algorithm. Some people say that the virus of COVID-19 is. The shape of the output from the Inception is (5, 5, 2048), which represents a feature vector of size 51200. All authors discussed the results and wrote the manuscript together. It also contributes to minimizing resource consumption which consequently, reduces the processing time. EMRes-50 model . 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). This stage can be mathematically implemented as below: In Eq. Finally, the sum of the features importance value on each tree is calculated then divided by the total number of trees as in Eq. Decaf: A deep convolutional activation feature for generic visual recognition. Abadi, M. et al. A. et al. Article One of the drawbacks of pre-trained models, such as Inception, is that its architecture required large memory requirements as well as storage capacity (92 M.B), which makes deployment exhausting and a tiresome task. D.Y. 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. Nature 503, 535538 (2013). The whole dataset contains around 200 COVID-19 positive images and 1675 negative COVID19 images. Our results indicate that the VGG16 method outperforms . (14)-(15) are implemented in the first half of the agents that represent the exploitation.

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