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

Abstract

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 situation: 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. However, 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 specialist 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%.

A Review of Recent Advances in Deep Learning Models for Chest Disease Detection Using Radiography

“…It was collected from COVID Chest X-ray dataset, Pediatric-CXR dataset and a kaggle repository [128]. Alqahtani et al [129] proposed a DL approach that uses readily available CXR images to identify COVID-19 cases. They employed an Inception-V4 model with transfer learning for the automatic detection of COVID-19 using CXR images.…”

“…We also found that transfer learning techniques were widely present in multiple experiments. They allowed obtaining higher results especially in the case of scarcity of data [129,132,134].…”

From Pixels to Pathology: Employing Computer Vision to Decode Chest Diseases in Medical Images

“…This study utilized data from two distinct chest X-ray datasets: Pediatric-CXR (containing images from 1,000 healthy children) and COVID-19 (504 images from patients with the virus). The proposed approach achieved an overall accuracy (ACC) of 99.63% in identifying COVID-19 infection [54]. In the effort to distinguish between typical and COVID-19 CXR images, a DL model named CovMnet was developed, as depicted in Figure 8 .5% [56].…”