Few-Shot Learning Approach on Tuberculosis Classification Based on Chest X-Ray Images
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866910609887461376 |
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| author | Pramana, A. A. G. Yogi Permana, Faiz Ihza Maulana, Muhammad Fazil Fudholi, Dzikri Rahadian |
| author_facet | Pramana, A. A. G. Yogi Permana, Faiz Ihza Maulana, Muhammad Fazil Fudholi, Dzikri Rahadian |
| contents | Tuberculosis (TB) is caused by the bacterium Mycobacterium tuberculosis, primarily affecting the lungs. Early detection is crucial for improving treatment effectiveness and reducing transmission risk. Artificial intelligence (AI), particularly through image classification of chest X-rays, can assist in TB detection. However, class imbalance in TB chest X-ray datasets presents a challenge for accurate classification. In this paper, we propose a few-shot learning (FSL) approach using the Prototypical Network algorithm to address this issue. We compare the performance of ResNet-18, ResNet-50, and VGG16 in feature extraction from the TBX11K Chest X-ray dataset. Experimental results demonstrate classification accuracies of 98.93% for ResNet-18, 98.60% for ResNet-50, and 33.33% for VGG16. These findings indicate that the proposed method outperforms others in mitigating data imbalance, which is particularly beneficial for disease classification applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_11644 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Few-Shot Learning Approach on Tuberculosis Classification Based on Chest X-Ray Images Pramana, A. A. G. Yogi Permana, Faiz Ihza Maulana, Muhammad Fazil Fudholi, Dzikri Rahadian Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Tuberculosis (TB) is caused by the bacterium Mycobacterium tuberculosis, primarily affecting the lungs. Early detection is crucial for improving treatment effectiveness and reducing transmission risk. Artificial intelligence (AI), particularly through image classification of chest X-rays, can assist in TB detection. However, class imbalance in TB chest X-ray datasets presents a challenge for accurate classification. In this paper, we propose a few-shot learning (FSL) approach using the Prototypical Network algorithm to address this issue. We compare the performance of ResNet-18, ResNet-50, and VGG16 in feature extraction from the TBX11K Chest X-ray dataset. Experimental results demonstrate classification accuracies of 98.93% for ResNet-18, 98.60% for ResNet-50, and 33.33% for VGG16. These findings indicate that the proposed method outperforms others in mitigating data imbalance, which is particularly beneficial for disease classification applications. |
| title | Few-Shot Learning Approach on Tuberculosis Classification Based on Chest X-Ray Images |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2409.11644 |