Retina disease detection: Datasets & Models

Manual grading of retinal images is time-consuming and requires trained ophthalmologists or graders, creating bottlenecks in screening programs where large volumes of images must be reviewed. Automating classification with computer vision helps triage cases, flag suspected diabetic retinopathy earlier, and extend screening reach in primary care and telemedicine settings. For healthcare imaging teams, a well-trained model provides consistent, repeatable grading that reduces inter-observer variability. This lets specialists focus their attention on cases most likely to need intervention.

A classification model integrates with existing fundus cameras and imaging pipelines, so screening workflows can be augmented without new capture hardware. Automated grading scales across high-volume screening programs and remote clinics while delivering consistent severity assessments. A diabetic retinopathy dataset paired with a ViT classifier can be retrained and refined over time to match your patient population and imaging conditions.

1. Upload an image: Drop any photo into the widget above to try retina disease detection for free, right on this page.
2. Preview instant results: The model detects retinal fundus image, microaneurysm, retinal hemorrhage, hard exudate, optic disc within seconds and returns an annotated image with structured predictions, so you can immediately see how well it works. Adjust the detection classes and re-run until the results fit your scenario.
3. Like what you see?: With a free Roboflow account, you can deploy this workflow with the Serverless Cloud API, customize it in your workspace, or train Classification (ViT) on your own images for production accuracy.
4. Get started for free: No code or training data required to begin: upload, review the results, and make it part of your stack in a single click.