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Chong et al.8 proposed an FS model, called Robustness-Driven FS (RDFS) to select futures from lung CT images to classify the patterns of fibrotic interstitial lung diseases. Chowdhury, M.E. etal. Design incremental data augmentation strategy for COVID-19 CT data. contributed to preparing results and the final figures. (22) can be written as follows: By taking into account the early mentioned relation in Eq. Continuing on my commitment to share small but interesting things in Google Cloud, this time I created a model for a An efficient feature generation approach based on deep learning and feature selection techniques for traffic classification. While, MPA, BPSO, SCA, and SGA obtained almost the same accuracy, followed by both bGWO, WOA, and SMA. 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. Scientific Reports Volume 10, Issue 1, Pages - Publisher. 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. The following stage was to apply Delta variants. used a dark Covid-19 network for multiple classification experiments on Covid-19 with an accuracy of 87% [ 23 ]. It is obvious that such a combination between deep features and a feature selection algorithm can be efficient in several image classification tasks. 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. Med. The classification accuracy of MPA, WOA, SCA, and SGA are almost the same. Figure6 shows a comparison between our FO-MPA approach and other CNN architectures. Whereas the worst one was SMA algorithm. Going deeper with convolutions. To obtain Figure7 shows the most recent published works as in54,55,56,57 and44 on both dataset 1 and dataset 2. The results are the best achieved on these datasets when compared to a set of recent feature selection algorithms. Stage 2: The prey/predator in this stage begin exploiting the best location that detects for their foods. 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 . In our experiment, we randomly split the data into 70%, 10%, and 20% for training, validation, and testing sets, respectively. They used different images of lung nodules and breast to evaluate their FS methods. \(r_1\) and \(r_2\) are the random index of the prey. Decis. Imaging 35, 144157 (2015). Heidari, A. 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. 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. In this work, the MPA is enhanced by fractional calculus memory feature, as a result, Fractional-order Marine Predators Algorithm (FO-MPA) is introduced. TOKYO, Jan 26 (Reuters) - Japan is set to downgrade its classification of COVID-19 to that of a less serious disease on May 8, revising its measures against the coronavirus such as relaxing. An image segmentation approach based on fuzzy c-means and dynamic particle swarm optimization algorithm. Methods: We employed a public dataset acquired from 20 COVID-19 patients, which . Lilang Zheng, Jiaxuan Fang, Xiaorun Tang, Hanzhang Li, Jiaxin Fan, Tianyi Wang, Rui Zhou, Zhaoyan Yan. org (2015). Article It can be concluded that FS methods have proven their advantages in different medical imaging applications19. 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. 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. kharrat and Mahmoud32proposed an FS method based on a hybrid of Simulated Annealing (SA) and GA to classify brain tumors using MRI. The algorithm combines the assessment of image quality, digital image processing and deep learning for segmentation of the lung tissues and their classification. All authors discussed the results and wrote the manuscript together. I. S. of Medical Radiology. 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. The accuracy measure is used in the classification phase. Generally, the most stable algorithms On dataset 1 are WOA, SCA, HGSO, FO-MPA, and SGA, respectively. For fair comparison, each algorithms was performed (run) 25 times to produce statistically stable results.The results are listed in Tables3 and4. Bibliographic details on CECT: Controllable Ensemble CNN and Transformer for COVID-19 image classification by capturing both local and global image features. Improving the ranking quality of medical image retrieval using a genetic feature selection method. 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. (5). Narayanan, S.J., Soundrapandiyan, R., Perumal, B. This combination should achieve two main targets; high performance and resource consumption, storage capacity which consequently minimize processing time. The model was developed using Keras library47 with Tensorflow backend48. The predator tries to catch the prey while the prey exploits the locations of its food. They concluded that the hybrid method outperformed original fuzzy c-means, and it had less sensitive to noises. The parameters of each algorithm are set according to the default values. Isolation and characterization of a bat sars-like coronavirus that uses the ace2 receptor. Figure5, shows that FO-MPA shows an efficient and faster convergence than the other optimization algorithms on both datasets. IEEE Trans. Inception architecture is described in Fig. Syst. Early diagnosis, timely treatment, and proper confinement of the infected patients are some possible ways to control the spreading of . 78, 2091320933 (2019). Slider with three articles shown per slide. CNNs are more appropriate for large datasets. 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). & Mahmoud, N. Feature selection based on hybrid optimization for magnetic resonance imaging brain tumor classification and segmentation. Both datasets shared some characteristics regarding the collecting sources. Authors Feature selection based on gaussian mixture model clustering for the classification of pulmonary nodules based on computed tomography. Health Inf. In our example the possible classifications are covid, normal and pneumonia. & Mirjalili, S. Slime mould algorithm: A new method for stochastic optimization. In57, ResNet-50 CNN has been applied after applying horizontal flipping, random rotation, random zooming, random lighting, and random wrapping on raw images. 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. This dataset consists of 219 COVID-19 positive images and 1341 negative COVID-19 images. To further analyze the proposed algorithm, we evaluate the selected features by FO-MPA by performing classification. Cancer 48, 441446 (2012). Softw. Initialize solutions for the prey and predator. Duan, H. et al. Da Silva, S. F., Ribeiro, M. X., Neto, Jd. 92, 103662. https://doi.org/10.1016/j.engappai.2020.103662 (2020). Syst. 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).. Meanwhile, the prey moves effectively based on its memory for the previous events to catch its food, as presented in Eq. 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: Metric learning Metric learning can create a space in which image features within the. Article Civit-Masot et al. \end{aligned} \end{aligned}$$, $$\begin{aligned} WF(x)=\exp ^{\left( {\frac{x}{k}}\right) ^\zeta } \end{aligned}$$, $$\begin{aligned}&Accuracy = \frac{\text {TP} + \text {TN}}{\text {TP} + \text {TN} + \text {FP} + \text {FN}} \end{aligned}$$, $$\begin{aligned}&Sensitivity = \frac{\text {TP}}{\text{ TP } + \text {FN}}\end{aligned}$$, $$\begin{aligned}&Specificity = \frac{\text {TN}}{\text {TN} + \text {FP}}\end{aligned}$$, $$\begin{aligned}&F_{Score} = 2\times \frac{\text {Specificity} \times \text {Sensitivity}}{\text {Specificity} + \text {Sensitivity}} \end{aligned}$$, $$\begin{aligned} Best_{acc} = \max _{1 \le i\le {r}} Accuracy \end{aligned}$$, $$\begin{aligned} Best_{Fit_i} = \min _{1 \le i\le r} Fit_i \end{aligned}$$, $$\begin{aligned} Max_{Fit_i} = \max _{1 \le i\le r} Fit_i \end{aligned}$$, $$\begin{aligned} \mu = \frac{1}{r} \sum _{i=1}^N Fit_i \end{aligned}$$, $$\begin{aligned} STD = \sqrt{\frac{1}{r-1}\sum _{i=1}^{r}{(Fit_i-\mu )^2}} \end{aligned}$$, https://doi.org/10.1038/s41598-020-71294-2. \end{aligned} \end{aligned}$$, $$\begin{aligned} \begin{aligned} U_{i}(t+1)&= \frac{1}{1!} Moreover, other COVID-19 positive images were added by the Italian Society of Medical and Interventional Radiology (SIRM) COVID-19 Database45. A., Fan, H. & Abd ElAziz, M. Optimization method for forecasting confirmed cases of covid-19 in china. In this experiment, the selected features by FO-MPA were classified using KNN. Biomed. Chong, D. Y. et al. Use the Previous and Next buttons to navigate the slides or the slide controller buttons at the end to navigate through each slide. 6, right), our approach still provides an overall accuracy of 99.68%, putting it first with a slight advantage over MobileNet (99.67 %). So some statistical operations have been added to exclude irrelevant and noisy features, and by making it more computationally efficient and stable, they are summarized as follows: Chi-square is applied to remove the features which have a high correlation values by computing the dependence between them. Med. . 40, 2339 (2020). We are hiring! Imaging 29, 106119 (2009). This algorithm is tested over a global optimization problem. A.A.E. 517 PDF Ensemble of Patches for COVID-19 X-Ray Image Classification Thiago Chen, G. Oliveira, Z. Dias Medicine Inspired by this concept, Faramarzi et al.37 developed the MPA algorithm by considering both of a predator a prey as solutions. So, based on this motivation, we apply MPA as a feature selector from deep features that produced from CNN (largely redundant), which, accordingly minimize capacity and resources consumption and can improve the classification of COVID-19 X-ray images. This stage can be mathematically implemented as below: In Eq. Blog, G. Automl for large scale image classification and object detection. To survey the hypothesis accuracy of the models. 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. In this subsection, the performance of the proposed COVID-19 classification approach is compared to other CNN architectures. 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 \(\delta\). https://www.sirm.org/category/senza-categoria/covid-19/ (2020). The variants of concern are Alpha, Beta, Gamma, and than the COVID-19 images. 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/)]. 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. Article 97, 849872 (2019). 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. (2) To extract various textural features using the GLCM algorithm. Taking into consideration the current spread of COVID-19, we believe that these techniques can be applied as a computer-aided tool for diagnosing this virus. After applying this technique, the feature vector is minimized from 2000 to 459 and from 2000 to 462 for Dataset1 and Dataset 2, respectively. However, it was clear that VGG19 and MobileNet achieved the best performance over other CNNs. Also, other recent published works39, who combined a CNN architecture with Weighted Symmetric Uncertainty (WSU) to select optimal features for traffic classification. is applied before larger sized kernels are applied to reduce the dimension of the channels, which accordingly, reduces the computation cost. 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. J. They showed that analyzing image features resulted in more information that improved medical imaging. Sci. implemented the FO-MPA swarm optimization and prepared the related figures and manuscript text. Comput. In this work, we have used four transfer learning models, VGG16, InceptionV3, ResNet50, and DenseNet121 for the classification tasks. In the current work, the values of k, and \(\zeta\) are set to 2, and 2, respectively. 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. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. https://doi.org/10.1016/j.future.2020.03.055 (2020). Covid-19 dataset. Rajpurkar, P. etal. The prey follows Weibull distribution during discovering the search space to detect potential locations of its food. Cite this article. Number of extracted feature and classification accuracy by FO-MPA compared to other CNNs on dataset 1 (left) and on dataset 2 (right). (8) at \(T = 1\), the expression of Eq. 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. and M.A.A.A. 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. arXiv preprint arXiv:2003.13815 (2020). The first one is based on Python, where the deep neural network architecture (Inception) was built and the feature extraction part was performed. 198 (Elsevier, Amsterdam, 1998). (4). 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. In this paper, after applying Chi-square, the feature vector is minimized for both datasets from 51200 to 2000. PubMed Decaf: A deep convolutional activation feature for generic visual recognition. Li, S., Chen, H., Wang, M., Heidari, A. \delta U_{i}(t)+ \frac{1}{2! Key Definitions. In this paper, we used two different datasets. 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. 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. Article Sahlol, A.T., Yousri, D., Ewees, A.A. et al. 4 and Table4 list these results for all algorithms. It also contributes to minimizing resource consumption which consequently, reduces the processing time. 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). Image Underst. For instance,\(1\times 1\) conv. 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. The whale optimization algorithm. 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. Future Gener. Moreover, from Table4, it can be seen that the proposed FO-MPA provides better results in terms of F-Score, as it has the highest value in datatset1 and datatset2 which are 0.9821 and 0.99079, respectively. Moreover, the \(R_B\) parameter has been changed to depend on weibull distribution as described below. For the exploration stage, the weibull distribution has been applied rather than Brownian to bost the performance of the predator in stage 2 and the prey velocity in stage 1 based on the following formula: Where k, and \(\zeta\) are the scale and shape parameters. Layers are applied to extract different types of features such as edges, texture, colors, and high-lighted patterns from the images. The family of coronaviruses is considered serious pathogens for people because they infect respiratory, hepatic, gastrointestinal, and neurologic diseases. Inceptions layer details and layer parameters of are given in Table1. They applied a fuzzy decision tree classifier, and they found that fuzzy PSO improved the classification accuracy. Internet Explorer). Google Scholar. \(\bigotimes\) indicates the process of element-wise multiplications. While no feature selection was applied to select best features or to reduce model complexity. Knowl. The proposed IMF approach is employed to select only relevant and eliminate unnecessary features. Also, image segmentation can extract critical features, including the shape of tissues, and texture5,6. and A.A.E. PubMed Central In this paper, we used TPUs for powerful computation, which is more appropriate for CNN. 41, 923 (2019). Chollet, F. Keras, a python deep learning library. For more analysis of feature selection algorithms based on the number of selected features (S.F) and consuming time, 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. 35, 1831 (2017). 101, 646667 (2019). 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]. 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. In Future of Information and Communication Conference, 604620 (Springer, 2020). Imaging Syst. COVID-19 image classification using deep features and fractional-order marine predators algorithm, $$\begin{aligned} \chi ^2=\sum _{k=1}^{n} \frac{(O_k - E_k)^2}{E_k} \end{aligned}$$, $$\begin{aligned} ni_{j}=w_{j}C_{j}-w_{left(j)}C_{left(j)}-w_{right(j)}C_{right(j)} \end{aligned}$$, $$\begin{aligned} fi_{i}=\frac{\sum _{j:node \mathbf \ {j} \ splits \ on \ feature \ i}ni_{j}}{\sum _{{k}\in all \ nodes }ni_{k}} \end{aligned}$$, $$\begin{aligned} normfi_{i}=\frac{fi_{i}}{\sum _{{j}\in all \ nodes }fi_{j}} \end{aligned}$$, $$\begin{aligned} REfi_{i}=\frac{\sum _{j \in all trees} normfi_{ij}}{T} \end{aligned}$$, $$\begin{aligned} D^{\delta }(U(t))=\lim \limits _{h \rightarrow 0} \frac{1}{h^\delta } \sum _{k=0}^{\infty }(-1)^{k} \begin{pmatrix} \delta \\ k\end{pmatrix} U(t-kh), \end{aligned}$$, $$\begin{aligned} \begin{pmatrix} \delta \\ k \end{pmatrix}= \frac{\Gamma (\delta +1)}{\Gamma (k+1)\Gamma (\delta -k+1)}= \frac{\delta (\delta -1)(\delta -2)\ldots (\delta -k+1)}{k! Jcs: An explainable covid-19 diagnosis system by joint classification and segmentation. 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). Recently, a combination between the fractional calculus tool and the meta-heuristics opens new doors in providing robust and reliable variants41. where CF is the parameter that controls the step size of movement for the predator. The updating operation repeated until reaching the stop condition. where \(R_L\) has random numbers that follow Lvy distribution. https://keras.io (2015). The predator uses the Weibull distribution to improve the exploration capability. By filtering titles, abstracts, and content in the Google Scholar database, this literature review was able to find 19 related papers to answer two research questions, i.e. IEEE Signal Process. PubMed Table3 shows the numerical results of the feature selection phase for both datasets. 111, 300323. Then, using an enhanced version of Marine Predators Algorithm to select only relevant features. 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. Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning. Coronavirus Disease (COVID-19): A primer for emergency physicians (2020) Summer Chavez et al. Automated detection of covid-19 cases using deep neural networks with x-ray images. Harikumar, R. & Vinoth Kumar, B. Math. Dual feature selection and rebalancing strategy using metaheuristic optimization algorithms in x-ray image datasets. Therefore, a feature selection technique can be applied to perform this task by removing those irrelevant features. The proposed IMF approach successfully achieves two important targets, selecting small feature numbers with high accuracy. 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 . In general, feature selection (FS) methods are widely employed in various applications of medical imaging applications. In 2018 IEEE International Symposium on Circuits and Systems (ISCAS), 15 (IEEE, 2018). 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. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. 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. Mirjalili, S. & Lewis, A. We can call this Task 2. For the special case of \(\delta = 1\), the definition of Eq. 95, 5167 (2016). Artif. Average of the consuming time and the number of selected features in both datasets. Mirjalili, S., Mirjalili, S. M. & Lewis, A. Grey wolf optimizer. The Shearlet transform FS method showed better performances compared to several FS methods. Medical imaging techniques are very important for diagnosing diseases. 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. 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 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). Future Gener. Future Gener. Also, in12, an Fs method based on SVM was proposed to detect Alzheimers disease from SPECT images. 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. 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 . The largest features were selected by SMA and SGA, respectively. 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 The results of max measure (as in Eq. Therefore, in this paper, we propose a hybrid classification approach of COVID-19. (14)(15) to emulate the motion of the first half of the population (prey) and Eqs. How- individual class performance. Automatic CNN-based Chest X-Ray (CXR) image classification for detecting Covid-19 attracted so much attention. Bukhari, S. U.K., Bukhari, S. S.K., Syed, A. wrote the intro, related works and prepare results. The proposed approach was evaluated on two public COVID-19 X-ray datasets which achieves both high performance and reduction of computational complexity. Nguyen, L.D., Lin, D., Lin, Z. where \(fi_{i}\) represents the importance of feature I, while \(ni_{j}\) refers to the importance of node j. 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. Chollet, F. Xception: Deep learning with depthwise separable convolutions. The whole dataset contains around 200 COVID-19 positive images and 1675 negative COVID19 images. The results indicate that all CNN-based architectures outperform the ViT-based architecture in the binary classification of COVID-19 using CT images. Correspondence to Its structure is designed based on experts' knowledge and real medical process. 2 (right). 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 . Stage 2 has been executed in the second third of the total number of iterations when \(\frac{1}{3}t_{max}< t< \frac{2}{3}t_{max}\). Efficient classification of white blood cell leukemia with improved swarm optimization of deep features. IEEE Trans. 11, 243258 (2007). According to the best measure, the FO-MPA performed similarly to the HHO algorithm, followed by SMA, HGSO, and SCA, respectively. I am passionate about leveraging the power of data to solve real-world problems. Our method is able to classify pneumonia from COVID-19 and visualize an abnormal area at the same time. Imag. Fractional Differential Equations: An Introduction to Fractional Derivatives, Fdifferential Equations, to Methods of their Solution and Some of Their Applications Vol. The 1360 revised papers presented in these proceedings were carefully reviewed and selected from . & 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.