Comparison of Classical and Deep Learning Models for Respiratory Sound Classification

This study aims to automatically classify normal and pathological respiratory sounds using a deep learning-based approach developed on the ICBHI 2017 respiratory sound dataset. MFCCs, Mel spectrograms, and spectral features were extracted from segmented respiratory sound recordings. These features were then used to train classical machine learning algorithms (XGBoost, SVM, KNN, Random Forest) and deep learning models (GhostNet v1–v4, EfficientNet-B0, ResNet50, MobileNetV3). The impact of data augmentation techniques was also systematically examined. The results demonstrate that the GhostNet v4 model achieved the highest performance with 89% accuracy and an F1-score of 0.89. This accuracy outperforms many existing methods reported in the literature using the same dataset. Confusion matrix analyses further indicate that the model reliably distinguishes between normal and pathological classes. These findings highlight the effectiveness of deep learning-based models in the automatic analysis of respiratory sounds and suggest promising solutions for integration into clinical decision support systems.