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. 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. 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. This combination should achieve two main targets; high performance and resource consumption, storage capacity which consequently minimize processing time. 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. 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 . As seen in Table1, we keep the last concatenation layer which contains the extracted features, so we removed the top layers such as the Flatten, Drop out and the Dense layers which the later performs classification (named as FC layer). Robustness-driven feature selection in classification of fibrotic interstitial lung disease patterns in computed tomography using 3d texture features. Article Harris hawks optimization: algorithm and applications. Access through your institution. 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. The test accuracy obtained for the model was 98%. Med. Computed tomography (CT) and magnetic resonance imaging (MRI) represent valuable input to AI algorithms, scanning human body sections for the sake of diagnosis. Comput. Comparison with other previous works using accuracy measure. 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. 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. Multimedia Tools Appl. Eng. Machine learning (ML) methods can play vital roles in identifying COVID-19 patients by visually analyzing their chest x-ray images. Netw. 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. While no feature selection was applied to select best features or to reduce model complexity. Dual feature selection and rebalancing strategy using metaheuristic optimization algorithms in x-ray image datasets. Some people say that the virus of COVID-19 is. FP (false positives) are the positive COVID-19 images that were incorrectly labeled as negative COVID-19, while FN (false negatives) are the negative COVID-19 images that were mislabeled as positive COVID-19 images. 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. 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. Keywords - Journal. Faramarzi et al.37 divided the agents for two halves and formulated Eqs. Fractional Differential Equations: An Introduction to Fractional Derivatives, Fdifferential Equations, to Methods of their Solution and Some of Their Applications Vol. 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 Very deep convolutional networks for large-scale image recognition. 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. We can call this Task 2. First: prey motion based on FC the motion of the prey of Eq. org (2015). To survey the hypothesis accuracy of the models. Moreover, we design a weighted supervised loss that assigns higher weight for . Imag. The second CNN architecture classifies the X-ray image into three classes, i.e., normal, pneumonia, and COVID-19. They applied the SVM classifier for new MRI images to segment brain tumors, automatically. So, transfer learning is applied by transferring weights that were already learned and reserved into the structure of the pre-trained model, such as Inception, in this paper. Also, they require a lot of computational resources (memory & storage) for building & training. Coronavirus disease (Covid-19) is an infectious disease that attacks the respiratory area caused by the severe acute . The prey follows Weibull distribution during discovering the search space to detect potential locations of its food. However, some of the extracted features by CNN might not be sufficient, which may affect negatively the quality of the classification images. The classification accuracy of MPA, WOA, SCA, and SGA are almost the same. Two real datasets about COVID-19 patients are studied in this paper. In 2019 26th National and 4th International Iranian Conference on Biomedical Engineering (ICBME), 194198 (IEEE, 2019). 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. For general case based on the FC definition, the Eq. In general, feature selection (FS) methods are widely employed in various applications of medical imaging applications. The combination of Conv. Besides, all algorithms showed the same statistical stability in STD measure, except for HHO and HGSO. E. B., Traina-Jr, C. & Traina, A. J. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Covid-19 dataset. Epub 2022 Mar 3. For fair comparison, each algorithms was performed (run) 25 times to produce statistically stable results.The results are listed in Tables3 and4. (4). }\delta (1-\delta )(2-\delta )(3-\delta ) U_{i}(t-3) + P.R\bigotimes S_i. Introduction Etymology. The 1360 revised papers presented in these proceedings were carefully reviewed and selected from . Da Silva, S. F., Ribeiro, M. X., Neto, Jd. Google Scholar. You are using a browser version with limited support for CSS. Donahue, J. et al. and pool layers, three fully connected layers, the last one performs classification. Rep. 10, 111 (2020). Finally, the sum of the features importance value on each tree is calculated then divided by the total number of trees as in Eq. 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. Kong, Y., Deng, Y. The Softmax activation function is used for this purpose because the output should be binary (positive COVID-19 negative COVID-19). The Weibull Distribution is a heavy-tied distribution which presented as in Fig. Get the most important science stories of the day, free in your inbox. They concluded that the hybrid method outperformed original fuzzy c-means, and it had less sensitive to noises. Based on54, the later step reduces the memory requirements, and improve the efficiency of the framework. The first one is based on Python, where the deep neural network architecture (Inception) was built and the feature extraction part was performed. Google Scholar. \delta U_{i}(t)+ \frac{1}{2! Liao, S. & Chung, A. C. Feature based nonrigid brain mr image registration with symmetric alpha stable filters. The . Currently, a new coronavirus, called COVID-19, has spread to many countries, with over two million infected people or so-called confirmed cases. Eq. 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. 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 . Moreover, the Weibull distribution employed to modify the exploration function. 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). 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. Covid-19 Classification Using Deep Learning in Chest X-Ray Images Abstract: Covid-19 virus, which has emerged in the Republic of China in an undetermined cause, has affected the whole world quickly. Automatic CNN-based Chest X-Ray (CXR) image classification for detecting Covid-19 attracted so much attention. Coronavirus Disease (COVID-19): A primer for emergency physicians (2020) Summer Chavez et al. 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. PubMedGoogle Scholar. Radiomics: extracting more information from medical images using advanced feature analysis. 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]. 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. Adv. FCM reinforces the ANFIS classification learning phase based on the features of COVID-19 patients. & Carlsson, S. Cnn features off-the-shelf: an astounding baseline for recognition. is applied before larger sized kernels are applied to reduce the dimension of the channels, which accordingly, reduces the computation cost. On January 20, 2023, Japanese Prime Minister Fumio Kishida announced that the country would be downgrading the COVID-19 classification. In COVID19 triage, DB-YNet is a promising tool to assist physicians in the early identification of COVID19 infected patients for quick clinical interventions. Apostolopoulos, I. D. & Mpesiana, T. A. Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks. 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. A deep feature learning model for pneumonia detection applying a combination of mRMR feature selection and machine learning models. For more analysis of feature selection algorithms based on the number of selected features (S.F) and consuming time, Fig. The whole dataset contains around 200 COVID-19 positive images and 1675 negative COVID19 images. Simonyan, K. & Zisserman, A. CAS Sci. Nature 503, 535538 (2013). Internet Explorer). A properly trained CNN requires a lot of data and CPU/GPU time. Hashim, F. A., Houssein, E. H., Mabrouk, M. S., Al-Atabany, W. & Mirjalili, S. Henry gas solubility optimization: a novel physics-based algorithm. Whereas, FO-MPA, MPA, HGSO, and WOA showed similar STD results. They were manually aggregated from various web based repositories into a machine learning (ML) friendly format with accompanying data loader code. Refresh the page, check Medium 's site status, or find something interesting. Afzali, A., Mofrad, F.B. It achieves a Dice score of 0.9923 for segmentation, and classification accuracies of 0. MATH Propose similarity regularization for improving C. Havaei, M. et al. Credit: NIAID-RML 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.. Heidari, A. International Conference on Machine Learning647655 (2014). Inspired by this concept, Faramarzi et al.37 developed the MPA algorithm by considering both of a predator a prey as solutions. Shi, H., Li, H., Zhang, D., Cheng, C. & Cao, X. Comput. The whale optimization algorithm. The \(\delta\) symbol refers to the derivative order coefficient. Narayanan, S.J., Soundrapandiyan, R., Perumal, B. Blog, G. Automl for large scale image classification and object detection. & Mahmoud, N. Feature selection based on hybrid optimization for magnetic resonance imaging brain tumor classification and segmentation. However, using medical imaging, chest CT, and chest X-ray scan can play a critical role in COVID-19 diagnosis. 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. The family of coronaviruses is considered serious pathogens for people because they infect respiratory, hepatic, gastrointestinal, and neurologic diseases. Classification Covid-19 X-Ray Images | by Falah Gatea | Medium 500 Apologies, but something went wrong on our end. For instance,\(1\times 1\) conv. Eng. 2. They used K-Nearest Neighbor (kNN) to classify x-ray images collected from Montgomery dataset, and it showed good performances. This task is achieved by FO-MPA which randomly generates a set of solutions, each of them represents a subset of potential features. Stage 1: After the initialization, the exploration phase is implemented to discover the search space. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate. Figure3 illustrates the structure of the proposed IMF approach. (33)), showed that FO-MPA also achieved the best value of the fitness function compared to others. Syst. Number of extracted feature and classification accuracy by FO-MPA compared to other CNNs on dataset 1 (left) and on dataset 2 (right). used VGG16 to classify Covid-19 and achieved good results with an accuracy of 86% [ 22 ]. Med. Tree based classifier are the most popular method to calculate feature importance to improve the classification since they have high accuracy, robustness, and simple38. Going deeper with convolutions. Test the proposed Inception Fractional-order Marine Predators Algorithm (IFM) approach on two publicity available datasets contain a number of positive negative chest X-ray scan images of COVID-19. Harikumar, R. & Vinoth Kumar, B. They applied a fuzzy decision tree classifier, and they found that fuzzy PSO improved the classification accuracy. 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. Future Gener. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 19 (2015). Regarding the consuming time as in Fig. IEEE Trans. IRBM https://doi.org/10.1016/j.irbm.2019.10.006 (2019). Methods: We employed a public dataset acquired from 20 COVID-19 patients, which . In this experiment, the selected features by FO-MPA were classified using KNN. layers is to extract features from input images. \end{aligned} \end{aligned}$$, $$\begin{aligned} \begin{aligned} U_{i}(t+1)&= \frac{1}{1!} Al-qaness, M. A., Ewees, A. Inspired by our recent work38, where VGG-19 besides statistically enhanced Salp Swarm Algorithm was applied to select the best features for White Blood Cell Leukaemia classification. Generally, the proposed FO-MPA approach showed satisfying performance in both the feature selection ratio and the classification rate. You have a passion for computer science and you are driven to make a difference in the research community? 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. To obtain It is obvious that such a combination between deep features and a feature selection algorithm can be efficient in several image classification tasks. Also, image segmentation can extract critical features, including the shape of tissues, and texture5,6. Table3 shows the numerical results of the feature selection phase for both datasets. 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. According to the best measure, the FO-MPA performed similarly to the HHO algorithm, followed by SMA, HGSO, and SCA, respectively. Imaging Syst. The authors declare no competing interests. The evaluation showed that the RDFS improved SVM robustness against reconstruction kernel and slice thickness. Deep-learning artificial intelligent (AI) methods have the potential to help improve diagnostic efficiency and accuracy for reading portable CXRs. To address this challenge, this paper proposes a two-path semi- supervised deep learning model, ssResNet, based on Residual Neural Network (ResNet) for COVID-19 image classification, where two paths refer to a supervised path and an unsupervised path, respectively. CAS 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. 35, 1831 (2017). 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! They applied the SVM classifier with and without RDFS. J. Med. Stage 2 has been executed in the second third of the total number of iterations when \(\frac{1}{3}t_{max}< t< \frac{2}{3}t_{max}\). Among the FS methods, the metaheuristic techniques have been established their performance overall other FS methods when applied to classify medical images. (22) can be written as follows: By using the discrete form of GL definition of Eq. An image segmentation approach based on fuzzy c-means and dynamic particle swarm optimization algorithm. Scientific Reports Volume 10, Issue 1, Pages - Publisher. Evaluation outcomes showed that GA based FS methods outperformed traditional approaches, such as filter based FS and traditional wrapper methods. EMRes-50 model . In this subsection, the performance of the proposed COVID-19 classification approach is compared to other CNN architectures. 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 results are the best achieved on these datasets when compared to a set of recent feature selection algorithms. 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). 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). Besides, the used statistical operations improve the performance of the FO-MPA algorithm because it supports the algorithm in selecting only the most important and relevant features. Slider with three articles shown per slide. 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. Key Definitions. 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. Comput. In the meantime, to ensure continued support, we are displaying the site without styles Moreover, a multi-objective genetic algorithm was applied to search for the optimal features subset. (2) calculated two child nodes. Fung, G. & Stoeckel, J. Svm feature selection for classification of spect images of alzheimers disease using spatial information. & Baby, C.J. Emphysema medical image classification using fuzzy decision tree with fuzzy particle swarm optimization clustering. Correspondence to The symbol \(r\in [0,1]\) represents a random number. 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. We are hiring! }\delta (1-\delta ) U_{i}(t-1)+ \frac{1}{3! 2 (left). \(r_1\) and \(r_2\) are the random index of the prey. https://doi.org/10.1016/j.future.2020.03.055 (2020). To further analyze the proposed algorithm, we evaluate the selected features by FO-MPA by performing classification. Stage 2: The prey/predator in this stage begin exploiting the best location that detects for their foods. Med. where CF is the parameter that controls the step size of movement for the predator. They also used the SVM to classify lung CT images. Bisong, E. Building Machine Learning and Deep Learning Models on Google Cloud Platform (Springer, Berlin, 2019). where \(R_L\) has random numbers that follow Lvy distribution. Scientific Reports (Sci Rep) Harikumar et al.18 proposed an FS method based on wavelets to classify normality or abnormality of different types of medical images, such as CT, MRI, ultrasound, and mammographic images. Support Syst. \(\bigotimes\) indicates the process of element-wise multiplications. Feature selection based on gaussian mixture model clustering for the classification of pulmonary nodules based on computed tomography. Luz, E., Silva, P.L., Silva, R. & Moreira, G. Towards an efficient deep learning model for covid-19 patterns detection in x-ray images. In ancient India, according to Aelian, it was . 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 \(\delta\). Eng. \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. Also, other recent published works39, who combined a CNN architecture with Weighted Symmetric Uncertainty (WSU) to select optimal features for traffic classification. For the image features that led to categorization, there were varied levels of agreement in the interobserver and intraobserver components that . The lowest accuracy was obtained by HGSO in both measures. Health Inf. Besides, the binary classification between two classes of COVID-19 and normal chest X-ray is proposed. Isolation and characterization of a bat sars-like coronavirus that uses the ace2 receptor. Dhanachandra, N. & Chanu, Y. J. They compared the BA to PSO, and the comparison outcomes showed that BA had better performance. chest X-ray images into three classes of COVID-19, normal chest X-ray and other lung diseases. Comput. Can ai help in screening viral and covid-19 pneumonia? 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). 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. Compared to59 which is one of the most recent published works on X-ray COVID-19, a combination between You Only Look Once (YOLO) which is basically a real time object detection system and DarkNet as a classifier was proposed. Also, As seen in Fig. and JavaScript. In such a case, in order to get the advantage of the power of CNN and also, transfer learning can be applied to minimize the computational costs21,22. & Wang, W. Medical image segmentation using fruit fly optimization and density peaks clustering. J. Clin. The memory terms of the prey are updated at the end of each iteration based on first in first out concept. One of the best methods of detecting. 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. Since its structure consists of some parallel paths, all the paths use padding of 1 pixel to preserve the same height & width for the inputs and the outputs. 132, 8198 (2018). Chollet, F. Xception: Deep learning with depthwise separable convolutions. Image Underst. 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. & Pouladian, M. Feature selection for contour-based tuberculosis detection from chest x-ray images. A. et al. From Fig. PVT-COV19D: COVID-19 Detection Through Medical Image Classification Based on Pyramid Vision Transformer. They employed partial differential equations for extracting texture features of medical images. 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