Diabetic retinopathy detection from fundus images: A wide survey from grading to segmentation of lesions

Diabetes is one of the most common diseases worldwide and requires accurate diagnosis. Patients with diabetes are often affected by diabetic retinopathy (DR), which can lead to low vision, vision loss, or blindness. Therefore, a robust computer-aided diagnosis system is needed to provide better treatment to patients. This review mainly focuses on the works related to diagnosing DR from retinal fundus images. A total of 128 research papers have been reviewed from 1986 to 2025. The survey is divided into two parts: one for the grading/classification of DR and the other for DR lesions segmentation. This survey article introduces the details of eye diseases, followed by the background details of DR and different imaging techniques required to diagnose DR, like fundus imaging, multifocal electroretinogram, and optical coherence tomography. Details of well-known DR datasets since 2009 are also provided, including their complete statistical information and potential dataset biases. Furthermore, the approaches used for grading and segmentation tasks from the early 1980s to recent developments are discussed. The reviewed papers are based on traditional and deep learning based methods used in DR diagnosis. In traditional methods, the researchers used image preprocessing, mathematical morphology, fuzzy system, active contour, features extraction methods, evolutionary approaches, and machine learning based classifiers. In deep learning, researchers have used convolutional neural network (CNN), long short-term memory, vision transformer, contrastive learning, federated learning, and Explainable Artificial Intelligence (XAI) based approaches for diagnosis. In this article, we have emphasized almost all the significant work done in diagnosing DR disease, the datasets used, and performance of methods on those datasets. The comparative analysis of the methods is also done to help researchers obtain future directions for further research in the area of medical disease identification, especially DR disease detection. The challenges of AI and its associated ethical implications are also discussed in the article to provide direction for future work.