ArticlePeerJ. Computer science2024
A systematic review on diabetic retinopathy detection and classification based on deep learning techniques using fundus images.
Article in PeerJ. Computer science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning-based optical coherence tomography and retinal images for detection of diabetic retinopathy: a systematic and meta analysis.Frontiers in endocrinology · 2025Pooled it
- RetinoMamba-AnatomyQuery: Anatomy-Guided Query Learning for Efficient Vision Impairment Screening from Retinal Fundus Images.Life (Basel, Switzerland) · 2026Article
- Comparative Analysis of CNN and Transformer Models for Multi-Class Diabetic Retinopathy Grading Using Fundus Images.Diagnostics (Basel, Switzerland) · 2026Article
- Validation of the Eyerobo FC Portable Fundus Camera for Diabetic Retinopathy Screening Using Public Datasets and Deep Learning.Ophthalmology and therapy · 2026Article
- An Improved Microaneurysms Detection for Diabetic Retinopathy Screening Using YOLO.Biomedicines · 2026Article
- Diabetic Retinopathy Detection: AI Models and Approaches.Journal of ophthalmology · 2026Review
- VG-RETFound: a hybrid retinal AI framework for early vision impairment screening through vessel-graph learning and cross-modal fusion.Frontiers in medicine · 2026Article
- Generative artificial intelligence in ophthalmology: a scoping review of current applications, opportunities, and challenges.Eye (London, England) · 2025Article
- Privacy preservation in diabetic disease prediction using federated learning based on efficient cross stage recurrent model.Scientific reports · 2025Article
- Forecasting the diabetic retinopathy progression using generative adversarial networks.Communications medicine · 2025Article
- RetinoDeep: Leveraging Deep Learning Models for Advanced Retinopathy Diagnostics.Sensors (Basel, Switzerland) · 2025Article
- Optimizing diabetic retinopathy detection with electric fish algorithm and bilinear convolutional networks.Scientific reports · 2025Article
- Artificial Intelligence Applications in Ophthalmology.JMA journal · 2025Review
- Optimized prediction of diabetes complications using ensemble learning with Bayesian optimization: a cost-efficient laboratory-based approach.Frontiers in endocrinology · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diabetic retinopathy (DR) is the leading cause of visual impairment globally. It occurs due to long-term diabetes with fluctuating blood glucose levels. It has become a significant concern for people in the working age group as it can lead to vision loss in the future. Manual examination of fundus images is time-consuming and requires much effort and expertise to determine the severity of the retinopathy. To diagnose and evaluate the disease, deep learning-based technologies have been used, which analyze blood vessels, microaneurysms, exudates, macula, optic discs, and hemorrhages also used for initial detection and grading of DR. This study examines the fundamentals of diabetes, its prevalence, complications, and treatment strategies that use artificial intelligence methods such as machine learning (ML), deep learning (DL), and federated learning (FL). The research covers future studies, performance assessments, biomarkers, screening methods, and current datasets. Various neural network designs, including recurrent neural networks (RNNs), generative adversarial networks (GANs), and applications of ML, DL, and FL in the processing of fundus images, such as convolutional neural networks (CNNs) and their variations, are thoroughly examined. The potential research methods, such as developing DL models and incorporating heterogeneous data sources, are also outlined. Finally, the challenges and future directions of this research are discussed.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.