Enhancing Lung Disease Diagnosis via Semi-Supervised Machine Learning

• URL: http://arxiv.org/abs/2507.16845v1
• Date: Sun, 20 Jul 2025 19:10:24 GMT
• Title: Enhancing Lung Disease Diagnosis via Semi-Supervised Machine Learning
• Authors: Xiaoran Xua, In-Ho Rab, Ravi Sankarc,
• Abstract summary: Lung diseases, including lung cancer and COPD, are significant health concerns globally.<n>This study investigates the use of semi supervised learning methods for lung sound signal detection using a model combination of MFCC+CNN.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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