A novel deep convolutional neural network model for COVID-19 disease detection

Abstract

The novel coronavirus, generally known as COVID19, is a new type of coronavirus which first appeared in Wuhan Province of China in December 2019. The biggest impact of this new coronavirus is its very high contagious feature which brings the life to a halt. As soon as data about the nature of this dangerous virus are collected, the research on the diagnosis of COVID-19 has started to gain a lot of momentum. Today, the gold standard for COVID-19 disease diagnosis is typically based on swabs from the nose and throat, which is time-consuming and prone to manual errors. The sensitivity of these tests are not high enough for early detection. These disadvantages show how essential it is to perform a fully automated framework for COVID-19 disease diagnosis based on deep learning methods using widely available X-ray protocols. In this paper, a novel, powerful and robust Convolutional Neural Network (CNN) model is designed and proposed for the detection of COVID-19 disease using publicly available datasets. This model is used to decide whether a given chest X-ray image of a patient has COVID-19 or not with an accuracy of 99.20%. Experimental results on clinical datasets show the effectiveness of the proposed model. It is believed that study proposed in this research paper can be used in practice to help the physicians for diagnosing the COVID-19 disease. © 2020 IEEE.