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Passang Doma Sherpa is a Tutor in the Department of Medical Radiology and Imaging Technology at Sumandeep College of Allied and Healthcare Science, Vadodara, Gujarat, India. Her professional responsibilities include teaching in medical radiology and imaging technology. Her academic role supports the development of knowledge in medical imaging. Her contribution to this chapter focuses on deep learning-based respiratory disease detection using X-ray images.
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.