A Deep Learning Approach to Respiratory Disease Classification Using Lung Sound Visualization for Telemedicine Applications
Abstract
This study presents the development of an intelligent system for the classification of respiratory diseases using lung sound visualizations and deep learning. A hybrid Convolutional Neural Network and Bidirectional Long Short-Term Memory (CNN–BiLSTM) model was designed to classify four conditions: asthma, bronchitis, tuberculosis, and normal (healthy). Lung sound recordings were converted into time-frequency representations (e.g., mel-spectrograms), enabling spatial-temporal feature extraction. The system achieved an overall classification accuracy of 99.5%, with F1-scores above 0.93 for all classes. The confusion matrix revealed minimal misclassifications, primarily between asthma and bronchitis. These results suggest that the proposed model can effectively support real-time, non-invasive respiratory screening, particularly in telemedicine environments. Future work includes clinical validation, integration of patient metadata, and adoption of transformer-based models to further enhance diagnostic performance.
References
Acharya, J., & Basu, A. (2020). Deep Neural Network for Respiratory Sound Classification in Wearable Devices Enabled by Patient Specific Model Tuning. IEEE Transactions on Biomedical Circuits and Systems, 14(3), 535–544. https://doi.org/10.1109/TBCAS.2020.2981172
Alqudah, A. M., Qazan, S., & Obeidat, Y. M. (2022). Deep learning models for detecting respiratory pathologies from raw lung auscultation sounds. Soft Computing, 26(24), 13405–13429. https://doi.org/10.1007/S00500-022-07499-6/METRICS
Bardou, D., Zhang, K., & Ahmad, S. M. (2018). Lung sounds classification using convolutional neural networks. Artificial Intelligence in Medicine, 88, 58–69. https://doi.org/10.1016/J.ARTMED.2018.04.008
Gairola, S., Tom, F., Kwatra, N., & Jain, M. (2021). RespireNet: A Deep Neural Network for Accurately Detecting Abnormal Lung Sounds in Limited Data Setting. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2021-January, 527–530. https://doi.org/10.1109/EMBC46164.2021.9630091
García-Ordás, M. T., Benítez-Andrades, J. A., García-Rodríguez, I., Benavides, C., & Alaiz-Moretón, H. (2020). Detecting Respiratory Pathologies Using Convolutional Neural Networks and Variational Autoencoders for Unbalancing Data. Sensors 2020, Vol. 20, Page 1214, 20(4), 1214. https://doi.org/10.3390/S20041214
Jaber, M. M., Abd, S. K., Shakeel, P. M., Burhanuddin, M. A., Mohammed, M. A., & Yussof, S. (2020). A telemedicine tool framework for lung sounds classification using ensemble classifier algorithms. Measurement, 162, 107883. https://doi.org/10.1016/J.MEASUREMENT.2020.107883
Jung, S. Y., Liao, C. H., Wu, Y. S., Yuan, S. M., & Sun, C. T. (2021). Efficiently Classifying Lung Sounds through Depthwise Separable CNN Models with Fused STFT and MFCC Features. Diagnostics 2021, Vol. 11, Page 732, 11(4), 732. https://doi.org/10.3390/DIAGNOSTICS11040732
Kim, Y., Hyon, Y. K., Jung, S. S., Lee, S., Yoo, G., Chung, C., & Ha, T. (2021). Respiratory sound classification for crackles, wheezes, and rhonchi in the clinical field using deep learning. Scientific Reports, 11(1), 1–11. https://doi.org/10.1038/S41598-021-96724-7;SUBJMETA=139,228,692,700;KWRD=DIAGNOSIS,HEALTH+SERVICES
Perna, D., & Tagarelli, A. (2019). Deep auscultation: Predicting respiratory anomalies and diseases via recurrent neural networks. Proceedings - IEEE Symposium on Computer-Based Medical Systems, 2019-June, 50–55. https://doi.org/10.1109/CBMS.2019.00020
Wanasinghe, T., Bandara, S., Madusanka, S., Meedeniya, D., Bandara, M., & De La Torre Díez, I. (2024). Lung Sound Classification for Respiratory Disease Identification Using Deep Learning: A Survey. International Journal of Online and Biomedical Engineering, 20(10), 115. https://doi.org/10.3991/IJOE.V20I10.49585
