Deep Learning for Chest X-Ray Classification: A Comprehensive Review of Methods, Advances, and Clinical Integration

Abstract

Deep learning transformed medical imaging and enabled the accurate and scalable diagnosis of respiratory infections such as COVID-19 and pneumonia. Unlike traditional radiology's weaknesses owing to overlapping characteristics and observer variability, CNNs are able to identify intricate visual patterns and achieve near-radiologist performance in multi-class CXR classification. Methodological innovations such as transfer learning, data augmentation, attention, and ensemble learning continue to enhance performance, with techniques to enhance interpretability like Grad-CAM advancing clinical trust. With significant advances already achieved, key challenges remain dataset imbalance, domain generalization, and computational cost. Directions for future research include the creation of standardized large-scale datasets, efficient model design for low-resource settings, and the fusion of imaging with clinical metadata. This review highlights recent achievements, current limitations, and potential directions in capitalizing on deep learning innovations into clinically reliable diagnostic tools.