Polyp detection: Datasets & Models

Manual polyp detection depends entirely on the endoscopist's attention across long procedures, and small or flat lesions are easy to miss during a single pass. A computer vision second reader continuously scans every frame, highlighting candidate polyps so clinicians can decide whether to inspect or resect them. In healthcare imaging settings this reduces adenoma miss rates, standardizes review quality across operators, and creates an auditable record of what was flagged during each exam.

Real-time bounding boxes let clinicians catch lesions during the procedure rather than on later review, and the model runs against the video from endoscopes and colonoscopes hospitals already own. Detection can be deployed at the edge for low-latency inference or batched over recorded studies, and it scales from a single suite to an entire endoscopy department without changing capture hardware.

1. Upload an image: Drop any photo into the widget above to try polyp detection for free, right on this page.
2. Preview instant results: The model detects polyp, adenoma, sessile lesion, colon mucosa 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 RF-DETR 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.