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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2509.09515 |
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| _version_ | 1866915490219163648 |
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| author | Kumar, Yoga Disha Sendhil Shetty, Manas V Vhaduri, Sudip |
| author_facet | Kumar, Yoga Disha Sendhil Shetty, Manas V Vhaduri, Sudip |
| contents | This paper investigates the effectiveness of few-shot learning for respiratory sound classification, focusing on coughbased detection of COVID-19, Flu, and healthy conditions. We leverage Prototypical Networks with spectrogram representations of cough sounds to address the challenge of limited labeled data. Our study evaluates whether few-shot learning can enable models to achieve performance comparable to traditional deep learning approaches while using significantly fewer training samples. Additionally, we compare multi-class and binary classification models to assess whether multi-class models can perform comparably to their binary counterparts. Experimental findings show that few-shot learning models can achieve competitive accuracy. Our model attains 74.87% accuracy in multi-class classification with only 15 support examples per class, while binary classification achieves over 70% accuracy across all class pairs. Class-wise analysis reveals Flu as the most distinguishable class, and Healthy as the most challenging. Statistical tests (paired t-test p = 0.149, Wilcoxon p = 0.125) indicate no significant performance difference between binary and multiclass models, supporting the viability of multi-class classification in this setting. These results highlight the feasibility of applying few-shot learning in medical diagnostics, particularly when large labeled datasets are unavailable. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_09515 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Cough Classification using Few-Shot Learning Kumar, Yoga Disha Sendhil Shetty, Manas V Vhaduri, Sudip Machine Learning This paper investigates the effectiveness of few-shot learning for respiratory sound classification, focusing on coughbased detection of COVID-19, Flu, and healthy conditions. We leverage Prototypical Networks with spectrogram representations of cough sounds to address the challenge of limited labeled data. Our study evaluates whether few-shot learning can enable models to achieve performance comparable to traditional deep learning approaches while using significantly fewer training samples. Additionally, we compare multi-class and binary classification models to assess whether multi-class models can perform comparably to their binary counterparts. Experimental findings show that few-shot learning models can achieve competitive accuracy. Our model attains 74.87% accuracy in multi-class classification with only 15 support examples per class, while binary classification achieves over 70% accuracy across all class pairs. Class-wise analysis reveals Flu as the most distinguishable class, and Healthy as the most challenging. Statistical tests (paired t-test p = 0.149, Wilcoxon p = 0.125) indicate no significant performance difference between binary and multiclass models, supporting the viability of multi-class classification in this setting. These results highlight the feasibility of applying few-shot learning in medical diagnostics, particularly when large labeled datasets are unavailable. |
| title | Cough Classification using Few-Shot Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.09515 |