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Bimal Nepal is a Medical Imaging Technologist at STNM Multispecialty Hospital, Government of Sikkim, Gangtok, India. His professional responsibilities involve medical imaging services in a multispecialty healthcare setting. His role contributes to diagnostic imaging and related clinical workflows. His contribution to this chapter concerns the use of X-ray imaging for AI-assisted respiratory disease analysis.
Pneumonia and various respiratory ailments significantly contribute to global morbidity and mortality, necessitating prompt identification and precise diagnosis for optimal patient care. Chest X-ray imaging is extensively utilised to assess respiratory irregularities due to its accessibility, affordability, and frequent application in clinical settings. Nonetheless, the interpretation of chest radiographs can be difficult owing to the convergence of radiological patterns, discrepancies in picture quality, and resemblances between infected and noninfectious pulmonary diseases. This section offers an extensive summary of pneumonia and respiratory illness identification with deep learning techniques applied to X-ray imagery. It examines chest radiography, accessible datasets, picture preprocessing techniques, data augmentation methods, transfer learning, CNN architectures, attention mechanisms, transformer-based methodologies, multiclass classification, explainable AI, and model assessment. The section additionally explores issues related to dataset bias, class imbalance, data leakage, diagnostic ambiguity, interpretability, and clinical implementation.