A Study of COVID-19 Diagnosis Applying Artificial Intelligence to X-Rays Images

Abstract

X-ray imaging, as a technique of non-destructive testing, has demonstrated considerable promise in COVID-19 diagnosis, particularly if supplemented with artificial intelligence (AI). Both radiologic technologists and AI researchers have raised the alarm about having to use increased doses of radiation in order to get more refined images and, hence, enhance diagnostic precision. In this research, we assess whether the disparity in exposure to the radiation dose considerably influences the credibility of AI-based diagnostic systems for COVID-19. A heterogeneous dataset of chest X-rays acquired at varying degrees of radiation exposure was run through four convolutional neural networks: VGG16, VGG19, ResNet50, and ResNet50V2. Results indicated above 91% accuracies, demonstrating that greater radiation exposure does not appreciably enhance diagnostic accuracy. Low radiation exposure sufficient to be utilized by human radiologists is therefore adequate for AI-based diagnosis. These findings are useful to the medical community, emphasizing that maximum diagnostic accuracy using AI does not need increased doses of radiation, thus further guaranteeing the safe application of X-ray imaging in COVID-19 diagnosis and possibly other medical and veterinary applications.