Rapid detection of COVID-19 from chest X-ray images using deep convolutional neural networks. (18th January 2023)

• Record Type: Journal Article
• Authors: Panigrahi, Sweta; Raju, U.S.N.; Pathak, Debanjan; Kadambari, K.V.; Ala, Harika
• Is Part Of: International journal of biomedical engineering and technology. Volume 41:Number 1(2023)
• Page Start: 1
• Page End: 15
• Publication Date: 2023-01-18
• Subjects: COVID-19 diagnosis -- chest X-ray images -- deep CNN -- transfer learning -- cross-validation; Biomedical engineering -- Periodicals; 610.28
• Languages: English
• ISSNs: 1752-6418

Abstract

The entire world is suffering from the corona pandemic (COVID-19) since December 2019. Deep convolutional neural networks (deep CNN) can be used to develop a rapid detection system of COVID-19. Among all the existing literature, ResNet50 is showing better performance, but with three main limitations, i.e.: 1) overfitting; 2) computation cost; 3) loss of feature information. To overcome these problems authors have proposed four different modifications on ResNet50, naming it as LightWeightResNet50. An image dataset containing chest X-ray images of coronavirus patients and normal persons is used for evaluation. Five-fold cross-validation is applied with transfer learning. Ten different performance measures (true positive, false negative, false positive, true negative, accuracy, recall, specificity, precision, F1-score and area under curve) are used for evaluation along with fold-wise performance measures comparison. The four proposed methods have an accuracy improvement of 4%, 13%, 14% and 7% respectively when compared with ResNet50.