A Novel Multi-Objective Rat Swarm Optimizer-Based Convolutional Neural Networks for the Diagnosis of COVID-19 Disease

Abstract

Early detection of coronavirus disease (COVID-19) is considered an essential task for disease control and cure. Thus, an automated diagnosis of COVID-19 is highly desirable. This paper introduces a novel diagnosis approach, namely, RSO-AlexNet-COVID-19. The proposed hybrid approach is based on the rat swarm optimizer (RSO) and convolutional neural network (CNN). RSO is used to find the optimal values for the hyperparameters of the AlexNet architecture to achieve a high level of diagnostic accuracy of COVID-19. It obtained overall classification accuracy of 100% for CT images datasets and an accuracy of 95.58% for the X-ray images dataset. Moreover, the performance of the proposed hybrid approach is compared with other CNN architecture, Inception v3, VGG16, and VGG19.