Enhancing Respiratory Disease Diagnosis with AI Lung Sound Analysis: A Web-Based Approach

Accurate and timely diagnosis of respiratory diseases remains a critical challenge in clinical practice, particularly in resource-limited and remote healthcare settings. This study proposes a web-based automated respiratory disease classification system leveraging a hybrid Convolutional Neural Network–Long Short-Term Memory with Time-Distributed (CNN-LSTM-TD) architecture for lung sound analysis. The proposed model integrates three complementary time-frequency representations—Mel-Frequency Cepstral Coefficients (MFCCs), Mel-spectrograms, and Chroma Short-Time Fourier Transform (Chroma-STFT)—to comprehensively capture both local spectral characteristics and long-range temporal dependencies inherent in respiratory cycles. Specifically, the TimeDistributed CNN block extracts localised acoustic features from sequential frames, while the LSTM layer models their temporal evolution, enabling robust identification of pathological acoustic signatures such as wheezes and crackles. The model was rigorously evaluated on the benchmark ICBHI 2017 dataset across six diagnostic categories: healthy, asthma, chronic obstructive pulmonary disease (COPD), pneumonia, upper respiratory tract infection (URTI), and bronchiectasis. The CNN-LSTM-TD model achieved an F1-score of 0.94, recall of 0.91, precision of 0.97, overall accuracy of 96.40%, and an AUC-ROC of 0.96, significantly outperforming standalone CNN, LSTM, and CNN-LSTM baseline models. The accompanying web interface supports audio file upload, real-time visualisation of waveforms and spectrograms, and confidence score reporting, collectively facilitating clinical decision support and telemedicine integration. These results demonstrate that the synergy of temporally aware deep feature extraction and accessible web deployment positions the proposed system as a clinically viable, scalable tool for automated respiratory disease diagnosis and remote patient monitoring.