An IoT-enabled smart health care system for screening of COVID-19 with multi layers features fusion and selection

Abstract

Advancement of smart medical sensors, devices, cloud computing, and health care technologies is getting remarkable attention from academia and the health care industry. As, Internet of things (IoT) has been recognized as one of the promising research topics in the domain of health care, particularly in medical image processing. Researchers utilized various machine and deep learning techniques along with artificial intelligence for analyzing medical images. These developed techniques are used to detect diseases that might assist medical experts in diagnosing diseases at early stages and providing accurate, consistent, effective, and speedy results, and decrease the mortality rate. Nowadays, Coronavirus (COVID-19) has been growing as one of the most rigorous and severe infections and spreading globally. Consequently, an intelligent automated system is required as an active diagnostic choice that might be used to prevent the spread of COVID-19. Thus, this work presented an IoT-enabled smart health care system for the automatic screening and classification of contagious diseases (Pneumonia, COVID-19) in Chest X-ray images. The developed system is based on two different deep learning architectures used with a multi-layers feature fusion and feature selection approach to classify X-ray images of infectious diseases. This work comprises the following steps: to enhance the diversity of the data set, data augmentation is performed, while for feature extraction, deep learning architectures, i.e., VGG-19 and Inception-V3, are used along with transfer learning. For the fusion of extracted features obtained from deep learning architectures, a parallel maximum covariance, and for feature selection, the multi-logistic regression controlled entropy variance approach is applied. For experimentation, a data set is customized containing Chest X-ray images using various publicly available resources. The system provides an overall classification accuracy of 97%.