Open Access


A Comprehensive Investigation of Machine Learning Feature Extraction and Classification Methods for Automated Diagnosis of COVID-19 Based on X-ray Images

Mazin Abed Mohammed1, Karrar Hameed Abdulkareem2, Begonya Garcia-Zapirain3, Salama A. Mostafa4, Mashael S. Maashi5, Alaa S. Al-Waisy1, Mohammed Ahmed Subhi6, Ammar Awad Mutlag7, Dac-Nhuong Le8,9,*
1 College of Computer Science and Information Technology, University of Anbar, Ramadi, 31001, Iraq
2 College of Agriculture, Al-Muthanna University, Samawah, 66001, Iraq
3 eVIDA Lab, University of Deusto, Bilbao, 48007, Spain
4 Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Johor, 86400, Malaysia
5 Software Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh, 11451, Saudi Arabia
6 Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, 43600, Malaysia
7 Pure Science Department, Ministry of Education, General Directorate of Curricula, Baghdad, 10, Iraq
8 Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam
9 Faculty of Information Technology, Duy Tan University, Da Nang, 550000, Vietnam
* Corresponding Author: Dac-Nhuong Le. Email:
(This article belongs to this Special Issue: Big Data, Analytics and Intelligent Algorithms for COVID-19)

Computers, Materials & Continua 2021, 66(3), 3289-3310.

Received 15 July 2020; Accepted 12 August 2020; Issue published 28 December 2020


The 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.


Coronavirus disease; COVID-19 diagnosis; machine learning; convolutional neural networks; resnet50; artificial neural network; support vector machine; X-ray images; feature transfer learning

Cite This Article

M. Abed Mohammed, K. Hameed Abdulkareem, B. Garcia-Zapirain, S. A. Mostafa, M. S. Maashi et al., "A comprehensive investigation of machine learning feature extraction and classification methods for automated diagnosis of covid-19 based on x-ray images," Computers, Materials & Continua, vol. 66, no.3, pp. 3289–3310, 2021.


This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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