ADOPT: automatic deep learning and optimization-based approach for detection of novel coronavirus COVID-19 disease using X-ray images

Abstract

In the hospital, because of the rise in cases daily, there are a small number of COVID-19 test kits available. For this purpose, a rapid alternative diagnostic choice to prevent COVID-19 spread among individuals must be implemented as an automatic detection method. In this article, the multi-objective optimization and deep learning-based technique for identifying infected patients with coronavirus using X-rays is proposed. J48 decision tree approach classifies the deep feature of corona affected X-ray images for the efficient detection of infected patients. In this study, 11 different convolutional neural network-based (CNN) models (AlexNet, VGG16, VGG19, GoogleNet, ResNet18, ResNet50, ResNet101, InceptionV3, InceptionResNetV2, DenseNet201 and XceptionNet) are developed for detection of infected patients with coronavirus pneumonia using X-ray images. The efficiency of the proposed model is tested using k-fold cross-validation method. Moreover, the parameters of CNN deep learning model are tuned using multi-objective spotted hyena optimizer (MOSHO). Extensive analysis shows that the proposed model can classify the X-ray images at a good accuracy, precision, recall, specificity and F1-score rates. Extensive experimental results reveal that the proposed model outperforms competitive models in terms of well-known performance metrics. Hence, the proposed model is useful for real-time COVID-19 disease classification from X-ray chest images. Communicated by Ramaswamy H. Sarma