Automated Bleeding Detection and Classification in Wireless Capsule Endoscopy with YOLOv8-X

TL;DR

This paper introduces a YOLOv8-X based model for simultaneous detection and classification of gastrointestinal bleeding in Wireless Capsule Endoscopy images, achieving high accuracy and mAP on a curated dataset.

Contribution

The study presents a unified YOLOv8-X model for both detection and classification of bleeding in WCE images, with extensive dataset curation and open-source implementation.

Findings

96.10% classification accuracy

76.8% mean Average Precision (mAP) at 0.5 IoU

Robust performance on diverse images

Abstract

Gastrointestinal (GI) bleeding, a critical indicator of digestive system disorders, re quires efficient and accurate detection methods. This paper presents our solution to the Auto-WCEBleedGen Version V1 Challenge, where we achieved the consolation position. We developed a unified YOLOv8-X model for both detection and classification of bleeding regions in Wireless Capsule Endoscopy (WCE) images. Our approach achieved 96.10% classification accuracy and 76.8% mean Average Precision (mAP) at 0.5 IoU on the val idation dataset. Through careful dataset curation and annotation, we assembled and trained on 6,345 diverse images to ensure robust model performance. Our implementa tion code and trained models are publicly available at https://github.com/pavan98765/Auto-WCEBleedGen.