<![CDATA[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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