Research Progress on Deep Learning in Field of Diabetic Retinopathy Classification

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. © 2024 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.