Optimizing a CNN–LSTM Model for Respiratory Disease Classification from Lung Sounds

Abstract

Respiratory diseases remain a significant health concern, while accurate identification of respiratory conditions from lung sounds is challenging because acoustic characteristics can overlap among disease categories. This study aims to optimize a Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) model for multiclass respiratory disease classification from lung sound recordings while addressing class imbalance and limited subject diversity. The study used the Lung Dataset containing 336 recordings from 112 subjects. After excluding subjects with combined diagnoses and consolidating low-frequency categories, 324 recordings from 108 subjects representing seven classes were retained. The audio signals were normalized, standardized to 20 s, and transformed into 64-bin Mel-spectrograms with 160 time steps. CNN was employed to extract time-frequency features, while LSTM modeled temporal dependencies. Model optimization was further evaluated using subject-level 5-fold Group Cross-Validation, data augmentation, Focal Loss, and Class-Balanced Cross-Entropy. The baseline CNN–LSTM achieved an accuracy of 34.85%, precision of 24.61%, recall of 34.85%, and F1-score of 28.22% in the initial evaluation, with a macro-F1 of 14.07%. In the extended cross-validation experiment, the baseline CNN–LSTM achieved a mean accuracy of 35.87 ± 5.43% and macro-F1 of 10.26 ± 3.50%. Among the imbalance-handling strategies, Class-Balanced Cross-Entropy produced the highest macro-F1 of 12.16%, compared with 10.26% for the baseline, although its overall accuracy decreased to 27.52%. The results indicate that the principal challenge is not solely the model architecture but also severe class imbalance and the limited number of independent subjects. Therefore, subject-level validation and class-sensitive evaluation metrics are essential for obtaining a more reliable assessment of respiratory sound classification models. The optimized CNN–LSTM framework demonstrates potential for automated respiratory disease classification, although further validation using larger and more diverse datasets is required before clinical application.