Deep Learning for X-ray Classification: A Comprehensive Review of Methods, Challenges, and Future Prospects

Integrating deep learning into medical imaging has significantly advanced diagnostic accuracy, particularly in classifying X-ray images for pulmonary conditions. This paper presents a comprehensive review of deep learning methodologies applied to X-ray classification, emphasizing their effectiveness in diagnosing diseases such as pneumonia, tuberculosis, and COVID-19. We explore various deep learning architectures, including Convolutional Neural Networks (CNNs), ResNet, VGG, and EfficientNet, highlighting their comparative performance in X-ray analysis. The study also examines key challenges, including dataset quality, class imbalance, and model interpretability, which impact the clinical applicability of these technologies. Furthermore, ethical considerations such as data privacy, algorithmic bias, and transparency in decision-making are discussed. By synthesizing current literature, we provide insights into the ongoing advancements in deep learning for medical imaging and outline future research directions to enhance model robustness and integration into clinical practice. This review aims to contribute to the evolving landscape of AI-driven medical diagnostics, underscoring the transformative potential of deep learning in radiology.