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Open Access
Development of a Medical Decision Support System for Lung Disease Diagnosis Using Deep Learning
(SDU University, 2025) Medeuova Zh.
Lung diseases such as Chronic Obstructive Pulmonary Disease (COPD), asthma, and pneumonia remain a leading cause of morbidity and mortality worldwide, with developing countries like Kazakhstan experiencing a rising burden due to environmental and lifestyle factors. Traditional diagnostic methods, while effective, are often limited by cost, accessibility, and dependence on expert interpretation, especially in low-resource settings. To address these challenges, this study proposes a novel artificial intelligence DL based decision support system that classifies lung conditions into three categories like normal, COPD. Using multimodal data: chest X-rays and lung sound recordings. The system utilizes a dual-stream deep learning architecture with late fusion to extract and integrate features from each modality independently, removing the need for synthetically paired datasets. This approach not only improves diagnostic scalability and accuracy but also provides a practical, low-cost solution for early lung disease detection, particularly in underserved regions. The model’s performance demonstrates the viability of using real-world, unpaired data for multimodal diagnostics, offering a significant step toward accessible, AI-driven respiratory healthcare