An efficient deep belief network for Detection of Coronavirus Disease COVID-19

Abstract

COVID-19 infection is one of the most dangerous respiratory viruses and the early detection of this disease reduces the speed of its spread among people . The goal of this virus is to infect the lung by creating white patchy shadows inside the lungs. This paper presents an intelligent method based on the deep learning technique to analyze the medical images of respiratory diseases . Two data set was used in this experiment first dataset is normal lungs taken from Kaggle data repository. While abnormal lungs taken from (https://github.com/muhammedtalo/COVID-19) .The results show that the proposed system identifies the COVID-19 cases with an accuracy of 90%.