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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2507.16845 |
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| _version_ | 1866915439011954688 |
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| author | Xu, Xiaoran Ra, In-Ho Sankar, Ravi |
| author_facet | Xu, Xiaoran Ra, In-Ho Sankar, Ravi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_16845 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhancing Lung Disease Diagnosis via Semi-Supervised Machine Learning Xu, Xiaoran Ra, In-Ho Sankar, Ravi Audio and Speech Processing Machine Learning Sound 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. |
| title | Enhancing Lung Disease Diagnosis via Semi-Supervised Machine Learning |
| topic | Audio and Speech Processing Machine Learning Sound |
| url | https://arxiv.org/abs/2507.16845 |