Comparative Analysis of COVID-19 X-ray Images Classification Using Convolutional Neural Network, Transfer Learning, and Machine Learning Classifiers Using Deep Features
R. Rajagopal
Abstract
A new type of coronavirus called (SARS-CoV-2) causes the COVID-19 coronavirus disease. The World Health Organization (WHO) declared this COVID-19 disease as pandemic because the disease got spread over several countries. At present situation, there is no medicine available for prevention or cure of the infectious disease. Samples taken from persons with COVID-19 symptoms are commonly tested using Reverse Transcription–Polymerase Chain Reaction (RT-PCR) process which is costlier and also take a minimum of 24 h to get the test result as either negative or positive. The proposed work suggests the possibility of using X-ray images of persons having COVID-19 symptoms to be classified as 1) healthy, 2) COVID-19 affected, or 3) Pneumonia affected. Experimentation is carried out with data samples from each category and classification done using Convolutional Neural Network (CNN), transfer learning using VGG Net, and machine learning techniques such as Support Vector Machine (SVM) and XGBoost which utilizes features extracted with the help of Convolutional Neural Network. Out of the models compared, the SVM with CNN extracted features was able to produce a highest precision, recall, F1-score and accuracy of 95.27, 94.52, 94.94, and 95.81%, respectively in identifying healthy, Pneumonia, and COVID-19 affected persons while experimented with 5-fold cross validation.