用迁移学习提升眼底图疾病分类准确率，助力早期筛查
Retinal Disease Classification from Fundus Images using CNN Transfer Learning
- 采用预训练VGG16模型进行迁移学习，提升分类性能
- 测试准确率达90.8%，加权F1-score达0.90，显著优于基线模型
- 适合医疗影像开发、临床辅助诊断系统研究者参考
视网膜疾病是全球可预防性失明的主要原因之一。基于眼底图像的自动化筛查有潜力扩大早期发现的覆盖范围，尤其在资源匮乏地区。本文提出一个可复现的深度学习流程，用于从公开眼底图像中进行二分类视网膜疾病风险判断。我们对比了基础卷积神经网络与基于预训练VGG16主干网络的迁移学习方法，并在保留数据上评估泛化能力。为应对类别不平衡问题，采用类别加权策略，报告了准确率、精确率、召回率、F1-score、混淆矩阵及ROC-AUC等标准指标。VGG16迁移学习模型在测试集上达到90.8%准确率，加权F1-score为0.90，显著优于基线CNN（83.1%准确率）。结果表明，迁移学习提升了判别能力，但仍存在对少数病患类别的敏感性不足问题。论文讨论了数据集特征、类别不平衡和阈值选择带来的实际限制，并为实现临床可靠筛查提供可复现性指导与改进方向。
原文摘要 · Abstract (English)
Retinal diseases remain among the leading preventable causes of visual impairment worldwide. Automated screening based on fundus image analysis has the potential to expand access to early detection, particularly in underserved populations. This paper presents a reproducible deep learning pipeline for binary retinal disease risk classification from publicly available fundus photographs. We implement and compare a baseline convolutional neural network with a transfer learning approach using a pretrained VGG16 backbone and evaluate generalization on held-out data. To address class imbalance, we apply class weighting and report standard classification metrics including accuracy, precision, recall, F1-score, confusion matrices, and ROC-AUC. The VGG16 transfer learning model achieves 90.8% test accuracy with a weighted F1-score of 0.90, substantially outperforming the baseline CNN (83.1% accuracy). Results indicate that transfer learning improves discrimination compared to a baseline CNN, while also revealing remaining challenges in sensitivity to minority disease cases. We discuss practical limitations related to dataset characteristics, class imbalance, and threshold selection, and provide guidance for reproducibility and future improvements for clinically reliable screening
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。