Performance Evaluation of Learning Models for the Prognosis of COVID-19

Abstract

COVID-19 has developed as a worldwide pandemic that needs ways to be detected. It is a communicable disease and is spreading widely. Deep learning and transfer learning methods have achieved promising results and performance for the detection of COVID-19. Therefore, a hybrid deep transfer learning technique has been proposed in this study to detect COVID-19 from chest X-ray images. The work done previously contains a very less number of COVID-19 X-ray images. However, the dataset taken in this work is balanced with a total of 28,384 X-ray images, having 14,192 images in the COVID-19 class and 14,192 images in the normal class. Experimental evaluations were conducted using a chest X-ray dataset to test the efficacy of the proposed hybrid technique. The results clearly reveal that the proposed hybrid technique attains better performance in comparison to the existing contemporary transfer learning and deep learning techniques.