Automated COVID-19 detection using Deep Convolutional Neural Network and Chest X-ray Images

Abstract

COVID-19 was previously identified as 2019-nCoV, however it was reclassified as severe acute respiratory syndrome coronavirus 2 by the International Committee on Taxonomy of Viruses (ICTV) (SARS-CoV-2). It was first discovered in Wuhan, China’s Hubei Province, and has since spread all over the world. The scientific community is working to develop COVID-19 detection technologies that are both quick and accurate. Chest x-ray imaging can aid in the early diagnosis of COVID-19 patients. In COVID-19 individuals, chest x-rays can indicate a variety of lung abnormalities, including lung consolidation, ground-glass opacity, and others. The COVID-19 biomarkers, however, must be identified by qualified and experienced radiologists. Each report must be inspected by the radiologist, which is a time-consuming procedure. The medical infrastructure is currently overburdened due to the huge volume of patients. In this study, we propose automatic COVID-19 identification in chest x-rays using a deep learning technique. COVID-19, pneumonia, and healthy x-rays are included in the dataset for the studies. The proposed model had an average accuracy and sensitivity of 97 percent. The obtained findings demonstrate that the model can compete with existing state-of-the-art models.