Tumor detection: Datasets & Models
Automatically locate and bounding-box tumors in medical images such as MRI, CT, and ultrasound scans. A detection model flags suspicious regions to support radiologists in review.
Tumor detection
This detection workflow performs tumor detection by drawing bounding boxes around suspected tumors, lesions, and abnormal masses in medical imaging inputs. It accepts standard radiology images such as brain MRI, chest CT, and ultrasound scans, then returns the location and class of each detected finding to support radiologist review.
Try it out
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Why automate tumor detection
Manual review of imaging studies is time-consuming and volume keeps growing, forcing radiologists in healthcare imaging settings to scan slice after slice for small or subtle masses that are easy to miss under fatigue. Automating tumor detection with computer vision provides a consistent second read that surfaces candidate regions instantly, helping prioritize urgent cases and reduce the chance a lesion goes unnoticed. Rather than replacing the clinician, the model acts as a triage and flagging layer that focuses expert attention where it counts. This shortens review time and supports more reproducible, quantifiable assessments across large study backlogs.
Key benefits
Teams get faster, more consistent localization of suspicious regions that integrates with existing PACS pipelines and imaging viewers rather than requiring new capture hardware. RF-DETR delivers strong detection accuracy on the small, low-contrast objects common in radiology while scaling across thousands of studies. The result is measurable reductions in review time and a repeatable workflow that can be retrained on your own annotated datasets.
How to use it
- Upload an image: Drop any photo into the widget above to try tumor detection for free, right on this page.
- Preview instant results: The model detects tumor, lesion, mass, nodule, cyst 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.
- 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.
- 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.
Recommended model for tumor detection
RF-DETR is state of the art for real-time object detection, instance segmentation, and keypoint detection, with higher accuracy and lower latency than comparable models, and it's Apache 2.0 licensed. When you're ready to build a production model for tumor detection, fine-tune it on your own labeled images, then deploy to a Serverless Cloud API endpoint.
Not sure where to start?
Describe your Tumor Detection project to Roboflow Agent.