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

2024/12/21 by P. Chandra Shekar, Shekar, Pavan C, Vivek Kanhangad +5
Medicine · #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Gastrointestinal Bleeding Diagnosis and Treatment

paper · pdf · doi:10.48550/arxiv.2412.16624

openalex publication_date 2024/12/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

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