用SMOTE提升医学图像分类中的迁移学习效果，改善数据不平衡问题。
Enhancing Transfer Learning for Medical Image Classification with SMOTE: A Comparative Study
- 将SMOTE与迁移学习结合，缓解医疗图像中的类别不平衡。
- 在糖尿病视网膜病变检测中，召回率提升5.43%，准确率提高1.97%。
- 适合需要高精度诊断的医疗影像分析场景，尤其关注小样本类别。
本文研究并改进了迁移学习（TL）在医学影像多标签分类中的应用，重点关注脑肿瘤识别和糖尿病视网膜病变分期检测。由于领域特异性挑战，基于ImageNet预训练模型的TL性能存在差异。我们在两个数据集（Brain Tumor MRI 和 APTOS 2019）上评估了MobileNet、Xception、InceptionV3、ResNet50和DenseNet201五种预训练模型。结果显示，TL在脑肿瘤分类中表现优异，接近最优指标；但在糖尿病视网膜病变检测中，因类别不平衡导致性能受限。为此，我们引入合成少数类过采样技术（SMOTE），与TL及传统机器学习方法结合，使准确率提升1.97%，召回率（敏感性）提高5.43%，特异性提升0.72%。结果表明，结合TL、重采样技术和传统机器学习，可有效应对数据不平衡，提升分类性能，为更精准可靠的医学图像分析提供路径，且仅需极少额外计算开销。
原文摘要 · Abstract (English)
This paper explores and enhances the application of Transfer Learning (TL) for multilabel image classification in medical imaging, focusing on brain tumor class and diabetic retinopathy stage detection. The effectiveness of TL-using pre-trained models on the ImageNet dataset-varies due to domain-specific challenges. We evaluate five pre-trained models-MobileNet, Xception, InceptionV3, ResNet50, and DenseNet201-on two datasets: Brain Tumor MRI and APTOS 2019. Our results show that TL models excel in brain tumor classification, achieving near-optimal metrics. However, performance in diabetic retinopathy detection is hindered by class imbalance. To mitigate this, we integrate the Synthetic Minority Over-sampling Technique (SMOTE) with TL and traditional machine learning(ML) methods, which improves accuracy by 1.97%, recall (sensitivity) by 5.43%, and specificity by 0.72%. These findings underscore the need for combining TL with resampling techniques and ML methods to address data imbalance and enhance classification performance, offering a pathway to more accurate and reliable medical image analysis and improved patient outcomes with minimal extra computation powers.
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