Blood cell detection: Datasets & Models

Manual blood cell counting under a microscope is slow, tedious, and prone to inter-observer variability, and it does not scale when labs process high volumes of slides. Automating the count with a detection model gives consistent, repeatable results and frees skilled technicians from repetitive counting work. In healthcare imaging contexts, a well-trained model can process a smear image in seconds and surface cell counts that would otherwise take minutes of careful visual inspection. Starting from an existing blood cell dataset lets teams prototype quickly instead of annotating from scratch.

You get faster, more consistent cell counts that integrate with existing microscopes and imaging pipelines through standard image inputs and API-driven inference. The RF-DETR architecture handles densely packed, overlapping cells well, making it a strong recommended model for crowded smear images. Because the workflow runs on ordinary captured slide images, it scales from a single lab bench to high-throughput screening without new hardware.

1. Upload an image: Drop any photo into the widget above to try blood cell detection for free, right on this page.
2. Preview instant results: The model detects red blood cell, white blood cell, platelet 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.