Optimizing 2D Input Representations and Sub-phase Fusion Strategies for Differential Diagnosis of Asthma and COPD Using CNN- and GRU-Based Networks
Researchers have developed deep learning models, specifically CNNs and GRUs, to differentiate between asthma and COPD using pulmonary sound data. The study optimized input representations like MFCC matrices and log-mel spectrograms, finding MFCCs to be superior. Adaptive-length windowing was crucial for handling inconsistent temporal dimensions in spectrograms, leading to the best cycle-based F1-score of 0.877 and subject-based F1-score of 0.855. AI
IMPACT Novel deep learning approaches show promise for more accurate differential diagnosis of respiratory conditions using audio data.