Hybrid deep learning and YAMNet features for asthma diagnosis from respiratory sounds

Abstract A prompt and precise diagnosis is necessary to lessen the worldwide burden of asthma, a common chronic respiratory condition. Spirometry is one of the resource-intensive conventional methods that frequently misclassifies cases because of overlapping symptoms with other respiratory illnesses. The Asthma Detection Dataset v2, which is openly accessible and comprises actual audio recordings of respiratory noises, is used in this work to demonstrate a hybrid deep learning architecture for automated asthma detection. After applying thorough preprocessing, which includes denoising, segmentation, and augmentation, the suggested pipeline employs a dual-path feature extraction technique that combines pretrained YAMNet embeddings with handmade acoustic descriptors (MFCCs, chromagram, ZCR, and spectral features). Atrous Spatial Pyramid Pooling (ASPP) and Squeeze-and-Excitation (SE) modules are used to further improve these embeddings, and a Multi-Layer Perceptron is used for classification. The proposed model achieved high performance, with an accuracy of 98.6% ± 0.14, an F1-score of 97.5% ± 0.14, and a macro-AUC of 99.1% ± 0.14 using stratified 5-fold cross-validation. SHAP-based interpretability and visualization (waveforms, spectrograms, t-SNE) verified that the model records clinically significant auditory biomarkers such as crackles and wheezes. These results indicate the potential of the proposed model as an explainable and scalable computer-aided asthma diagnostic tool.