Deep learning in Chest X-Ray Pneumonia Diagnosis: A Review of Research Advances

Abstract

 Pneumonia, an acute respiratory infection affecting over 450 million people annually, remains a leading cause of global mortality. Chest X-ray (CXR) imaging serves as the primary diagnostic tool, yet its reliance on subjective radiologist interpretation often leads to delays and inconsistencies, particularly in resource-limited settings. Recent advancements in deep learning (DL) have revolutionized pneumonia diagnosis by enabling automated, high-accuracy analysis of CXR images. This review systematically examines the evolution of DL architectures for pneumonia detection, including convolutional neural networks (CNNs), vision transformers (ViTs), hybrid models, and emerging vision foundation models (VFMs). The author highlights their respective strengths, such as CNNs' localized feature extraction and ViTs' global context modeling, while addressing critical challenges like data scarcity, model interpretability, and computational barriers. Furthermore, the author discusses future directions, emphasizing hybrid model designs, federated learning for data diversity, and interpretability enhancements to bridge the gap between AI and clinical practice. By overcoming these challenges, DL-based diagnostic systems can achieve broader adoption, ultimately improving early detection and patient outcomes in both developed and developing regions.