Deep Learning-Based Prediction of COVID-19 and Viral Pneumonia from Chest X-Ray Images

Abstract

Abstract: In recent times, the novel Coronavirus disease (COVID-19) has emerged as one of the most infectious diseases, causing significant public health crises across over 200 nations worldwide. Given the challenges associated with the timeconsuming and error-prone nature of detecting COVID-19 through Reverse Transcription-Polymerase Chain Reaction (RTPCR), there is a growing reliance on alternative methods, such as examining chest X-ray (CXR) images. Viral pneumonia symptoms include a persistent cough with mucus, fever, chills, shortness of breath, and chest pain, especially during deep breaths or coughing. These symptoms often overlap significantly with those of other respiratory infections, including COVID-19. Accurately predicting COVID-19 severity and distinguishing it from viral pneumonia is crucial for effective patient management. Deep learning models offer promise in automating this process. The chest X-ray (CXR) images undergo preprocessing through Contrast Limited Adaptive Histogram Equalization (CLAHE) to improve their quality. These enhanced images are fed into ResNet50 and EfficientNet-B0, both renowned deep learning models. Comparative evaluation demonstrates ResNet50 achieving an accuracy of 92.58%, whereas EfficientNet-B0 achieves a higher accuracy of 93.08%. This study underscores the efficacy of deep learning in COVID-19 prediction. The findings suggest EfficientNet-B0’s potential for improved diagnostic accuracy. This methodology presents a promising approach for automated, accurate COVID-19 severity prediction and differentiation from viral pneumonia, aiding timely medical interventions.