A Transfer Learning Based Approach for COVID-19 Detection Using Inception-v4 Model

Coronavirus (COVID-19 or SARS-CoV-2) is a novel viral infection that started in December 2019 and has erupted rapidly in more than 150 countries. The rapid spread of COVID-19 has caused a global health emergency and resulted in governments imposing lock-downs to stop its transmission. There is a significant increase in the number of patients infected, resulting in a lack of test resources and kits in most countries. To overcome this panicked state of affairs, researchers are looking forward to some effective solutions to overcome this situa-tion: one of the most common and effective methods is to examine the X-radiation (X-rays) and computed tomography (CT) images for detection of Covid-19. How-ever, this method burdens the radiologist to examine each report. Therefore, to reduce the burden on the radiologist, an effective, robust and reliable detection system has been developed, which may assist the radiologist and medical specia-list in effective detecting of COVID. We proposed a deep learning approach that uses readily available chest radio-graphs (chest X-rays) to diagnose COVID-19 cases. The proposed approach applied transfer learning to the Deep Convolutional Neural Network (DCNN) model, Inception-v4, for the automatic detection of COVID-19 infection from chest X-rays images. The dataset used in this study contains 1504 chest X-ray images, 504 images of COVID-19 infection, and 1000 normal images obtained from publicly available medical repositories. The results showed that the proposed approach detected COVID-19 infection with an overall accuracy of 99.63. © 2023, Tech Science Press. All rights reserved.