Lung nodule detection: Datasets & Models

Manual review of chest CT studies is time-consuming and small nodules are easy to miss across hundreds of slices, especially under high reading volumes in busy radiology departments. Automating lung nodule detection provides a consistent second set of eyes that surfaces candidate findings, reduces the chance of an overlooked early-stage nodule, and lets radiologists focus attention where it matters. For healthcare imaging teams building screening and triage tools, a detection model turns raw scans into structured, reviewable candidates rather than requiring exhaustive slice-by-slice inspection.

An RF-DETR detection model can be trained on your own annotated CT and radiograph datasets and deployed against existing PACS and DICOM pipelines without new imaging hardware. It scales to high study volumes, produces consistent bounding-box output for downstream review or measurement, and can be retrained as you add annotated cases. The result is faster triage, reproducible flagging of suspicious findings, and a foundation for research and screening workflows.

1. Upload an image: Drop any photo into the widget above to try lung nodule detection for free, right on this page.
2. Preview instant results: The model detects lung nodule, pulmonary mass, ground-glass opacity, calcification, pleural effusion 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.