Optimizing 2D Input Representations and Sub-phase Fusion Strategies for Differential Diagnosis of Asthma and COPD Using CNN- and GRU-Based Networks

1. 1Apply adaptive-length windowing to fix temporal dimensions
2. 2Configure CNN- and GRU-based networks for pulmonary sound classification
3. 3Test the performance of VAR model against MFCC matrices and log-mel spectrograms
4. 4Build a deep learning model using optimized 2D input representations
5. 5Run experiments to evaluate the effectiveness of sub-phase fusion strategies

Data scientists and researchers on a healthcare team can benefit from this study to develop more accurate diagnostic models, while software engineers can apply the findings to build more effective deep learning-based systems

💡 Adaptive-length windowing can help fix inconsistent temporal dimensions in spectrogram-based representations, leading to more accurate pulmonary sound classification

💡 Improve asthma and COPD diagnosis with optimized 2D input representations and sub-phase fusion strategies using CNN- and GRU-based networks