Improved deep neural network algorithm for COVID-19 detection in the Internet of Things

In this paper, we propose an automatic detection system for COVID-19 cases based on the Internet of Things. In the proposed model, first, using Internet of Things technology, medical images are sent directly to the data collection after the suspicious person's visit through medical equipment equipped with Internet of Things, and then, in order to help radiologists to interpret medical images better, usage has been made of four pre-trained convolutional neural network models i.e. InceptionV3, InceptionResNetV2, VGG19 and ResNet152 as well as two datasets of chest radiology medical images and CT Scan in a 3-class classification for accurate prediction of cases suffering from COVID-19, healthy people, and diseased cases. Finally, the best result for CT-Scan images is related to InceptionResNetV2 architecture with an accuracy of 99.366%, and for radiology images related to the InceptionV3 architecture, it is 96.943%. The results show that this system leads to a reduction in daily visits to medical centers and thus reduces the pressure on the medical care system. It also helps rheology specialists to identify the disease as quickly as possible.