Mohammed, Mazin AbedAbdulkareem, Karrar HameedGarcía-Zapirain, BegoñaMostafa, Salama A.Maashi, Mashael S.Al-Waisy, Alaa S.Subhi, Mohammed AhmedMutlag, Ammar AwadLe, Dac-Nhuong2026-03-132026-03-132021Mohammed, M. A., Abdulkareem, K. H., Garcia-Zapirain, B., Mostafa, S. A., Maashi, M. S., Al-Waisy, A. S., Subhi, M. A., Mutlag, A. A., & Le, D.-N. (2021). A comprehensive investigation of machine learning feature extraction and classification methods for automated diagnosis of COVID-19 based on X-ray images. Computers, Materials and Continua, 66(3), 3289-3310. https://doi.org/10.32604/CMC.2021.0128741546-221810.32604/CMC.2021.012874https://hdl.handle.net/20.500.14454/5434The quick spread of the Coronavirus Disease (COVID-19) infection around the world considered a real danger for global health. The biological structure and symptoms of COVID-19 are similar to other viral chest maladies, which makes it challenging and a big issue to improve approaches for efficient identification of COVID-19 disease. In this study, an automatic prediction of COVID-19 identification is proposed to automatically discriminate between healthy and COVID-19 infected subjects in X-ray images using two successful moderns are traditional machine learning methods (e.g., artificial neural network (ANN), support vector machine (SVM), linear kernel and radial basis function (RBF), k-nearest neighbor (k-NN), Decision Tree (DT), and CN 2 rule inducer techniques) and deep learning models (e.g., MobileNets V2, ResNet50, GoogleNet, DarkNet and Xception). A large X-ray dataset has been created and developed, namely the COVID-19 vs. Normal (400 healthy cases, and 400 COVID cases). To the best of our knowledge, it is currently the largest publicly accessible COVID-19 dataset with the largest number of X-ray images of confirmed COVID-19 infection cases. Based on the results obtained from the experiments, it can be concluded that all the models performed well, deep learning models had achieved the optimum accuracy of 98.8% in ResNet50 model. In comparison, in traditional machine learning techniques, the SVM demonstrated the best result for an accuracy of 95% and RBF accuracy 94% for the prediction of coronavirus disease 2019.engArtificial neural networkConvolutional neural networksCoronavirus diseaseCOVID-19 diagnosisFeature transfer learningMachine learningResnet50Support vector machineX-ray imagesjournal article2026-03-131546-2226