CT-Scan Method-based Artificial Neural Network for Diagnosis of COVID-19

Abstract

The Covid-19 epidemic appeared suddenly, with a rapid start and leaping steps, declaring a threat to global health where it was the beginnings of its upbringing in Wuhan, China. Where the World Health Organization announced after confirming the results of human infections in December 2019 that it hurts all aspects of life in general and human health in particular. Therefore, it requires addressing such an epidemic quickly and with tight steps to avoid aggravating the situation, especially the lack of appropriate treatment. The necessity necessitated the use of quarantine for the injured and social distancing, in addition to the use of preventive measures such as masks, hand sterilization, non-contact, and leaving a safe distance. This paper aims to use an ANN algorithm based on CT and some laboratory and clinical parameters to determine whether a person is infected with Covid-19 or not. The results showed that two hidden layers were chosen for the ANN algorithm, where the first hidden layer was installed with ten nodes, while the second hidden layer was selected with five nodes once, ten nodes again, fifteen nodes, and twenty nodes. The results showed the best two hidden layers 10-20 nodes, and the accuracy was 99.43%.