A comprehensive investigation of machine learning feature extraction and classification methods for automated diagnosis of COVID-19 based on X-ray images

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dc.contributor.authorMohammed, Mazin Abed
dc.contributor.authorAbdulkareem, Karrar Hameed
dc.contributor.authorGarcía-Zapirain, Begoña
dc.contributor.authorMostafa, Salama A.
dc.contributor.authorMaashi, Mashael S.
dc.contributor.authorAl-Waisy, Alaa S.
dc.contributor.authorSubhi, Mohammed Ahmed
dc.contributor.authorMutlag, Ammar Awad
dc.contributor.authorLe, Dac-Nhuong
dc.date.accessioned2026-03-13T10:44:43Z
dc.date.available2026-03-13T10:44:43Z
dc.date.issued2021
dc.date.updated2026-03-13T10:44:43Z
dc.description.abstractThe 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.en
dc.identifier.citationMohammed, 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.2021.012874
dc.identifier.doi10.32604/CMC.2021.012874
dc.identifier.eissn1546-2226
dc.identifier.issn1546-2218
dc.identifier.urihttps://hdl.handle.net/20.500.14454/5434
dc.language.isoeng
dc.publisherTech Science Press
dc.subject.otherArtificial neural network
dc.subject.otherConvolutional neural networks
dc.subject.otherCoronavirus disease
dc.subject.otherCOVID-19 diagnosis
dc.subject.otherFeature transfer learning
dc.subject.otherMachine learning
dc.subject.otherResnet50
dc.subject.otherSupport vector machine
dc.subject.otherX-ray images
dc.titleA comprehensive investigation of machine learning feature extraction and classification methods for automated diagnosis of COVID-19 based on X-ray imagesen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.endPage3310
oaire.citation.issue3
oaire.citation.startPage3289
oaire.citation.titleComputers, Materials and Continua
oaire.citation.volume66
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