TY  - EJOU
AU  - Mohammed, Mazin Abed 
AU  - Abdulkareem, Karrar Hameed 
AU  - Garcia-Zapirain, Begonya 
AU  - Mostafa, Salama A. 
AU  - Maashi, Mashael S. 
AU  - Al-Waisy, Alaa S. 
AU  - Subhi, Mohammed Ahmed 
AU  - Mutlag, Ammar Awad 
AU  - Le, Dac-Nhuong 

TI  - A Comprehensive Investigation of Machine Learning Feature Extraction and Classification Methods for Automated Diagnosis of COVID-19 Based on X-ray Images
T2  - Computers, Materials \& Continua

PY  - 2021
VL  - 66
IS  - 3
SN  - 1546-2226

AB  - 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 <i>vs.</i> 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.
KW  - Coronavirus disease; COVID-19 diagnosis; machine learning; convolutional neural networks; resnet50; artificial neural network; support vector machine; X-ray images; feature transfer learning

DO  - 10.32604/cmc.2021.012874