Advancements and Challenges in Deep Learning for Chronic-Obstructive Pulmonary-Disease (COPD) Diagnosis

Deep Learning (DL) has revolutionized medical image analysis, particularly in diagnosing Chronic Obstructive Pulmonary Disease (COPD) and other pulmonary conditions using Chest X-Rays (CXRs). Despite these advancements, challenges such as noise artifacts, feature extraction limitations, and class imbalance continue to affect model performance and diagnostic accuracy. This survey provides a comprehensive review of recent DL-based approaches in medical image analysis, focusing on preprocessing techniques (e.g., histogram equalization, gamma correction), feature extraction methods (e.g., convolutional neural networks, transfer learning), and classification strategies (e.g., ensemble models, hybrid approaches). The findings reveal that recent methodologies have significantly improved diagnostic accuracy and model robustness, with techniques like gamma correction and hybrid feature extraction demonstrating notable performance gains. However, persistent challenges such as class imbalance and noise interference necessitate further research to optimize preprocessing, enhance feature extraction, and develop effective class-balancing strategies. By addressing these issues, future advancements in DL can lead to more reliable and interpretable medical diagnostic systems.