用半监督学习提升肺部声音诊断准确率，减少人工标注依赖。
Enhancing Lung Disease Diagnosis via Semi-Supervised Machine Learning
- 结合MFCC与CNN，引入Mix Match等半监督模块增强模型性能。
- 准确率达92.9%，比基线模型提升3.8%。
- 适合医疗数据少、标注成本高的场景使用。
肺部疾病（如肺癌和慢阻肺）是全球重大健康问题，传统诊断方法成本高、耗时长且具有侵入性。本研究探索利用半监督学习方法进行肺音信号检测，采用MFCC+CNN模型架构，并引入Mix Match、Co-Refinement和Co Refurbishing等半监督模块，旨在提升检测性能同时降低对人工标注的依赖。实验表明，加入半监督模块后，MFCC+CNN模型准确率达到92.9%，较基线模型提升3.8%。该研究针对个体差异大、标注数据不足等挑战，为肺部疾病声音检测提供了有效解决方案。
原文摘要 · Abstract (English)
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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