Intelligent Classification of Respiratory Diseases Using Machine Learning-Based Lung Sound Analysis
Respiratory diseases such as asthma, chronic obstructive pulmonary disease (COPD), pneumonia, bronchiectasis, bronchiolitis and upper respiratory tract infection (URTI) remain among the leading causes of illness and death worldwide. Conventional diagnosis relies heavily on auscultation and spirometry, both of which depend on clinician experience and are susceptible to human error. This paper presents a machine learning-based system for automated respiratory disease classification using lung sound recordings. Audio signals are resampled, segmented into temporal snippets, denoised through DFT-based baseline wander removal and amplitude-normalized, before being converted into log-scaled Mel spectrograms and Gammatone Cepstral Coefficient (GTCC) features. A lightweight Inception-based deep learning model (RDsLINet) classifies the processed signals into seven categories — Asthma, Bronchiectasis, Bronchiolitis, COPD, Healthy, Pneumonia and URTI. Complementary experiments using a Convolutional Neural Network and a ResNet-50 transfer-learning model on chest X-ray images were also carried out for comparison. The proposed audio-based pipeline achieved a classification accuracy of up to 99.22%, while the image-based ResNet-50 model achieved 96.8% accuracy. The trained model is deployed through a lightweight web interface that allows a user to upload a lung sound recording and receive a real-time diagnostic prediction with a downloadable report. The results suggest that combining spectral audio features with a lightweight deep-learning architecture can provide a fast, non-invasive and scalable decision-support tool for respiratory-disease screening.