Real‑Time Polyp Spotting Made Simple

A new tool called BCP‑YOLO turns a heavy image‑recognition model into a quick, clear system that can find colorectal polyps while doctors are looking at the colon.

The original YOLOv8 model is powerful but slow for live use, so BCP‑YOLO trims unnecessary parts and adds explanations of why it marks a spot.

Polyps are tricky: they come in many shapes, blend into the surrounding tissue, and often hide behind folds or glare.
Because of these challenges, many existing methods miss polyps or lag too long for practical help during a procedure.

BCP‑YOLO tackles the problem by simplifying the neural network so it runs faster on standard computers.
It also keeps a lightweight interpretability layer that shows the image areas influencing each detection, giving doctors confidence in the AI’s choices.

The system was tested on a large set of colonoscopy videos, and it found polyps with similar accuracy to the full YOLOv8 while being up to ten times quicker.

Doctors can use BCP‑YOLO as a real‑time assistant, pointing the camera at the colon and receiving instant alerts when a suspicious bump appears.
The added transparency helps clinicians understand whether the AI is right or if they should double‑check a spot.

Because it runs on ordinary hardware, the technology could be adopted in many clinics without expensive upgrades.