Lung diseases, including lung cancer and COPD, are significant health concerns globally. Traditional diagnostic methods can be costly, time-consuming, and invasive. This study investigates the use of semi supervised learning methods for lung sound signal detection using a model combination of MFCC+CNN. By introducing semi supervised learning modules such as Mix Match, Co-Refinement, and Co Refurbishing, we aim to enhance the detection performance while reducing dependence on manual annotations. With the add-on semi-supervised modules, the accuracy rate of the MFCC+CNN model is 92.9%, an increase of 3.8% to the baseline model. The research contributes to the field of lung disease sound detection by addressing challenges such as individual differences, feature insufficient labeled data.
@article{arxiv.2507.16845,
title = {Enhancing Lung Disease Diagnosis via Semi-Supervised Machine Learning},
author = {Xiaoran Xu and In-Ho Ra and Ravi Sankar},
journal= {arXiv preprint arXiv:2507.16845},
year = {2025}
}