AINeutralarXiv – CS AI · 7h ago5/10
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Optimizing 2D Input Representations and Sub-phase Fusion Strategies for Differential Diagnosis of Asthma and COPD Using CNN- and GRU-Based Networks
This study evaluates machine learning approaches for distinguishing asthma from COPD using pulmonary sound analysis, comparing MFCC matrices, log-mel spectrograms, and VAR models with CNN and GRU networks. MFCC representations with adaptive-length windowing achieved the best performance (F1-score 0.877), while sophisticated fusion strategies and data augmentation unexpectedly degraded results, emphasizing the importance of authentic clinical data.