Improved Bi-Channel CNN For Covid-19 Diagnosis

Abstract

The Covid-19 virus, which initially originated in Wuhan, China, was declared a pandemic by the World Health Organization on March 11, 2020. Since then, it has had a tremendous impact on human health and the World economy. Rapid identification and treatment of the disease have been a prime concern. Analysis of Radiographic Chest X-ray images has become an effective way to determine the disease and its severity. This paper proposes a low complex methodology that uses Convolutional Neural Networks (CNN) for classifying three types of X-ray images, Covid-19, Healthy and Viral Pneumonia. The architecture consists of two channels: the main channel with four convolutional layers with increasing order of filter size and a side channel with two convolutional layers of the same filter size. The architecture performs well with an overall accuracy of 95.24% and with only 89,41,783 parameters. It has been compared with different deep CNN s and several state-of-the-art works of literature.