用少样本学习提升肺结核X光片分类准确率，解决数据不平衡问题。
Few-Shot Learning Approach on Tuberculosis Classification Based on Chest X-Ray Images
- 采用原型网络实现少样本学习，缓解肺结核数据集不平衡。
- ResNet-18在TBX11K数据集上达98.93%准确率，远超VGG16的33.33%。
- 适合医疗影像少样本分类任务，尤其对罕见病检测有实用价值。
肺结核由结核分枝杆菌引起，主要影响肺部。早期检测对提高治疗效果和降低传播风险至关重要。人工智能，特别是通过胸部X光片图像分类，可辅助肺结核检测。然而，肺结核胸部X光数据集中的类别不平衡问题给准确分类带来挑战。本文提出一种基于原型网络的少样本学习（FSL）方法以应对这一问题。我们比较了ResNet-18、ResNet-50和VGG16在TBX11K胸部X光数据集上的特征提取性能。实验结果表明，ResNet-18分类准确率达98.93%，ResNet-50为98.60%，VGG16仅为33.33%。结果表明，所提方法在缓解数据不平衡方面优于其他方法，特别适用于疾病分类应用。
原文摘要 · Abstract (English)
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.
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