Enhancing Lung Disease Diagnosis via Semi-Supervised Machine Learning

Xiaoran Xu; In-Ho Ra; Ravi Sankar

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arXiv · 2507.16845

Enhancing Lung Disease Diagnosis via Semi-Supervised Machine Learning

Xiaoran Xu·In-Ho Ra·Ravi Sankar

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Abstract

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.

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BibTeX RIS

Xiaoran Xu, In-Ho Ra, Ravi Sankar. 2025-07-20. Enhancing Lung Disease Diagnosis via Semi-Supervised Machine Learning. https://arxiv.org/abs/2507.16845

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