Abstract
Diabetic retinopathy (DR) is a leading cause of vision loss worldwide, and early diagnosis through automated retinal image analysis can significantly reduce the risk of blindness. This paper presents a robust deep learning framework for both binary and five-class DR classification, leveraging transfer learning and extensive data augmentation to address the challenges of class imbalance and limited training data. We evaluate a range of pretrained convolutional neural network architectures, including variants of ResNet and EfficientNet, on the APTOS 2019 dataset. For binary classification, our proposed model achieves a state-of-the-art accuracy of 98.9%, with a precision of 98.6%, recall of 99.3%, F1-score of 98.9%, and an AUC of 99.4%. In the more challenging five-class severity classification task, our model obtains a competitive accuracy of 84.6% and an AUC of 94.1%, outperforming several existing approaches. Our findings also demonstrate that EfficientNet-B0 and ResNet34 offer optimal trade-offs between accuracy and computational efficiency across both tasks. These results underscore the effectiveness of combining class-balanced augmentation with transfer learning for high-performance DR diagnosis. The proposed framework provides a scalable and accurate solution for DR screening, with potential for deployment in real-world clinical environments.
Abstract (translated)
糖尿病视网膜病变（DR）是全球视力丧失的主要原因之一，通过自动化视网膜图像分析进行早期诊断可以显著降低失明的风险。本文提出了一种稳健的深度学习框架，用于二分类和五级分级的DR分类，利用迁移学习和广泛的数据增强来解决类别不平衡和训练数据有限的问题。我们在APTOS 2019数据集上评估了多种预训练卷积神经网络架构，包括ResNet及其变体、EfficientNet等。对于二分类任务，我们的模型实现了业内领先的准确率（98.9%），精确度为98.6%，召回率为99.3%，F1分数为98.9%，AUC值为99.4%。在更具挑战性的五级分级严重程度分类任务中，我们的模型获得了竞争力的准确性（84.6%）和AUC（94.1%），超越了多种现有方法。此外，研究还表明EfficientNet-B0和ResNet34在这两项任务上都能提供最优的精度与计算效率之间的平衡。这些结果强调了结合类别均衡增强技术与迁移学习对于实现高性能DR诊断的有效性。提出的框架为DR筛查提供了可扩展且准确的解决方案，并具备在实际临床环境中部署的应用潜力。
URL
https://arxiv.org/abs/2507.17121
