Automated Bleeding Detection and Classification in Wireless Capsule Endoscopy with YOLOv8-X
This work addresses efficient bleeding detection for medical diagnostics, but it is incremental as it applies an existing method to a specific challenge.
The paper tackled automated detection and classification of gastrointestinal bleeding in wireless capsule endoscopy images using a YOLOv8-X model, achieving 96.10% classification accuracy and 76.8% mAP on a validation dataset.
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.