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.
Abstract (translated)
肺部疾病，包括肺癌和慢性阻塞性肺病（COPD），是全球性的重大健康问题。传统诊断方法成本高、耗时且具有侵入性。本研究探讨了使用半监督学习方法检测肺音信号的有效性，采用MFCC+CNN模型组合。通过引入半监督学习模块如Mix Match、Co-Refinement和Co Refurbishing，我们旨在提高检测性能的同时减少对人工标注的依赖。在添加这些半监督模块后，MFCC+CNN模型的准确率达到了92.9%，相比基线模型提高了3.8%。该研究通过解决个体差异和特征不足等问题，为肺部疾病声音检测领域做出了贡献。
URL
https://arxiv.org/abs/2507.16845
