Utilizing Deep Learning Models and Transfer Learning for COVID-19 Detection from X-Ray Images

• Published in: SN computer science Vol. 4; no. 4; p. 326
• Main Authors: Agrawal, Shubham, Honnakasturi, Venkatesh, Nara, Madhumitha, Patil, Nagamma
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.01.2023; Springer Nature B.V
• Subjects: Accuracy; Automation; Cementing; Classification; Computed tomography; Computer Imaging; Computer Science; Computer Systems Organization and Communication Networks; Coronaviruses; COVID-19; Data Structures and Information Theory; Datasets; Deep learning; Disease transmission; Enabling Innovative Computational Intelligence Technologies for IOT; Feature selection; Information Systems and Communication Service; Literature reviews; Machine learning; Medical equipment; Neural networks; Original Research; Pandemics; Pattern Recognition and Graphics; Pneumonia; Software Engineering/Programming and Operating Systems; Test equipment; Vision; X-rays; Deep learning; Coronavirus (COVID-19); Chest X-ray; LIME; Grad-CAM; Classification; Heatmap
• ISSN: 2661-8907; 2662-995X; 2661-8907
• DOI: 10.1007/s42979-022-01655-3

COVID-19 has been a global pandemic. Flattening the curve requires intensive testing, and the world has been facing a shortage of testing equipment and medical personnel with expertise. There is a need to automate and aid the detection process. Several diagnostic tools are currently being used for COVID-19, including X-Rays and CT-scans. This study focuses on detecting COVID-19 from X-Rays. We pursue two types of problems: binary classification (COVID-19 and No COVID-19) and multi-class classification (COVID-19, No COVID-19 and Pneumonia). We examine and evaluate several classic models, namely VGG19, ResNet50, MobileNetV2, InceptionV3, Xception, DenseNet121, and specialized models such as DarkCOVIDNet and COVID-Net and prove that ResNet50 models perform best. We also propose a simple modification to the ResNet50 model, which gives a binary classification accuracy of 99.20% and a multi-class classification accuracy of 86.13%, hence cementing the ResNet50’s abilities for COVID-19 detection and ability to differentiate pneumonia and COVID-19. The proposed model’s explanations were interpreted via LIME which provides contours, and Grad-CAM, which provides heat-maps over the area(s) of interest of the classifier, i.e., COVID-19 concentrated regions in the lungs, and realize that LIME explains the results better. These explanations support our model’s ability to generalize. The proposed model is intended to be deployed for free use.