Detecting COVID-19 and Lung Diseases Utilizing Parallel CNN Model on Chest X-Rays Enhanced with GradCAM Visualization

The emergence of COVID-19 has underscored the urgency of accurate medical diagnosis, particularly in the context of chest X-ray image classification for various lung conditions, including COVID-19, normal cases, viral pneumonia, and lung opacity. In light of the pandemic's impact, there is a pressing need to develop resource-efficient solutions for automated classification, specifically tailored for resource-constrained devices. This article addresses this need by proposing a Lightweight Parallel CNN (Convolutional Neural Network) architecture capable of effectively categorizing the aforementioned multi-class cases. Leveraging a comprehensive dataset of 21165 images, our model achieves a decent accuracy of 93.21% for four-class classification. Notably, the proposed architecture maintains efficiency with fewer than 783k parameters, demonstrating its potential for deployment on devices with limited computational resources. This research contributes to the advancement of accessible and accurate medical diagnostics, catering to the demand for streamlined and reliable solutions amidst challenging circumstances.