Implementation of a Hybrid CNN-BiLSTM-Attention Model for Detecting Respiratory Sound Abnormalities

Abstract

Early detection of respiratory disorders generally relies on digital stethoscope devices which are expensive and have limited availability. Although the use of smartphones offers a highly practical alternative for independent screening purposes in the community, respiratory sounds recorded directly through built-in microphones are highly susceptible to environmental noise and acoustic characteristic differences (domain shift). Consequently, artificial intelligence (AI) models trained exclusively on public medical datasets often fail to adapt in real-world clinical scenarios, yielding an initial accuracy rate of only 57.78%. To address this critical limitation, this study proposes the application of a Transfer Learning technique with a Full Fine-Tuning approach on a hybrid Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM) architecture. Furthermore, an Attention Mechanism is specifically integrated so the model can focus its computational weight on very short-duration spectral anomalies that are often masked by background noise. Following the pre-training phase, the model was comprehensively calibrated using 45 primary respiratory sound samples from smartphones. The testing results demonstrated a substantial performance leap, with the overall accuracy rapidly increasing to 93.33%. The proposed system successfully identified all abnormal pulmonary cases without a single miss, achieving 100% Sensitivity. This finding Indicates that the integration of CNN-BiLSTM and Attention Mechanism is promising in handling acoustic distortion. Thus, the model is highly viable to be implemented as an early clinical decision support system with a showing high sensitivity on the evaluated localized cohort predictions.

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