Abstract: Diabetic retinopathy is one of the primary causes of visual impairment in diabetic patients, and early classification and diagnosis are of significant importance for disease management and control. Deep learning methods have the capability to automatically extract features of retinal lesions and perform classification, making them essential tools for diabetic retinopathy classification. This paper begins by introducing commonly used datasets and evaluation metrics for diabetic retinopathy, summarizing the applications of deep learning in binary classification of diabetic retinopathy. It then provides an overview of various classical deep learning models used for severity classification of diabetic retinopathy, focuses on the classification and diagnosis methods of convolutional neural networks, and makes a comprehensive comparative analysis of different approaches. Finally, the paper discusses the challenges in this field and provides an outlook on future directions for research and development.

Key words: diabetic retinopathy, deep learning, binary classification, severity classification, convolutional neural network(CNN)

摘要： 糖尿病视网膜病变是导致糖尿病患者视力受损的主要原因之一，早期的分类诊断对于病情的治疗与控制具有重要意义。深度学习方法能够自动提取视网膜病变的特征并进行分类，因此成为糖尿病视网膜病变分类的重要工具。介绍常用的糖尿病视网膜病变数据集及评价指标，总结了深度学习在糖尿病视网膜病变二分类中的应用；综述了不同的经典深度学习模型在糖尿病视网膜病变严重程度分类中的应用，重点阐述卷积神经网络的分类诊断方法，并对不同方法进行综合对比分析；最后讨论该领域面临的挑战，并对未来发展方向进行了展望。

关键词: 糖尿病视网膜病变, 深度学习, 二分类, 严重程度分类, 卷积神经网络（CNN）