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

• Published in: Computers, materials & continua Vol. 66; no. 3; pp. 3289 - 3310
• Main Authors: Abed Mohammed, Mazin, Hameed Abdulkareem, Karrar, Garcia-Zapirain, Begonya, A. Mostafa, Salama, S. Maashi, Mashael, S. Al-Waisy, Alaa, Ahmed Subhi, Mohammed, Awad Mutlag, Ammar, Le, Dac-Nhuong
• Format: Journal Article
• Language: English
• Published: Henderson Tech Science Press 2021
• Subjects: Accuracy; Artificial neural networks; Coronaviruses; COVID-19; Datasets; Decision trees; Deep learning; Feature extraction; Image classification; Learning theory; Machine learning; Medical imaging; Model accuracy; Public health; Radial basis function; Signs and symptoms; Support vector machines; Viral diseases
• ISSN: 1546-2226; 1546-2218; 1546-2226
• DOI: 10.32604/cmc.2021.012874

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.