XCR-Net: A Computer Aided Framework to Detect COVID-19

Abstract

Coronavirus disease (COVID-19) has been the most challenging public health issue during the past years. The current computer-aided methods of COVID19 detection face difficulty to distinguish between COVID19 and pneumonia since they share common symptoms. Traditional methods for solving binary classification problems with COVID-19 classes are limited in their calibre to balance efficiency and accuracy. On the other hand, medical devices like reverse transcription polymerase chain reaction (RT-PCR) take longer than an hour to produce test results, and Rapid Antigen Testing (RAT) is less effective at detecting COVID-19 because it can produce false positive or false negative results. The biggest challenges here are efficiency and accuracy. To address these issues, this study introduces a novel deep multi-layer COVID19 chest X-ray based lung contamination recognition network (XCR-Net) to detect COVID-19, pneumonia, and normal individuals. Our proposed XCR-Net has been tested with five different chest X-ray datasets, having normal, COVID19, and pneumonia case chest X-ray images, and the consistency of XCR-Net has been verified by a 10-fold cross validation scheme. This multi-class study reports the class-wise and overall performance of XCR-Net, and it outperforms all other multi-class COVID-19 endeavours. Future biomedical researchers and IT professionals will be able to advance chest X-ray research with the help of the envisioned XCR-Net.