Patient Domain Supervised Contrastive Learning for Lung Sound Classification Using Mobile Phone
This work addresses lung disease diagnosis accessibility by leveraging smartphones, though it is incremental with a modest gain.
The study tackled lung sound classification using smartphone microphones by developing Patient Domain Supervised Contrastive Learning (PD-SCL) integrated with the Audio Spectrogram Transformer (AST), achieving a 2.4% performance improvement over the original AST model.
Auscultation is crucial for diagnosing lung diseases. The COVID-19 pandemic has revealed the limitations of traditional, in-person lung sound assessments. To overcome these issues, advancements in digital stethoscopes and artificial intelligence (AI) have led to the development of new diagnostic methods. In this context, our study aims to use smartphone microphones to record and analyze lung sounds. We faced two major challenges: the difference in audio style between electronic stethoscopes and smartphone microphones, and the variability among patients. To address these challenges, we developed a method called Patient Domain Supervised Contrastive Learning (PD-SCL). By integrating this method with the Audio Spectrogram Transformer (AST) model, we significantly improved its performance by 2.4\% compared to the original AST model. This progress demonstrates that smartphones can effectively diagnose lung sounds, addressing inconsistencies in patient data and showing potential for broad use beyond traditional clinical settings. Our research contributes to making lung disease detection more accessible in the post-COVID-19 world.