Title: Rapid detection of COVID-19 from chest X-ray images using deep convolutional neural networks

Authors: Sweta Panigrahi; U.S.N. Raju; Debanjan Pathak; K.V. Kadambari; Harika Ala

Addresses: National Institute of Technology Warangal, Warangal-506004, Telangana State, India ' National Institute of Technology Warangal, Warangal-506004, Telangana State, India ' National Institute of Technology Warangal, Warangal-506004, Telangana State, India ' National Institute of Technology Warangal, Warangal-506004, Telangana State, India ' Institute of Aeronautical Engineering, Hyderabad, Telangana State, India

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.

Keywords: COVID-19 diagnosis; chest X-ray images; deep CNN; transfer learning; cross-validation.

DOI: 10.1504/IJBET.2023.128510

International Journal of Biomedical Engineering and Technology, 2023 Vol.41 No.1, pp.1 - 15

Received: 30 May 2020
Accepted: 21 Sep 2020
Published online: 25 Jan 2023 *