用YOLOv8-X实现胶囊内镜出血区域检测与分类，准确率超96%。
Automated Bleeding Detection and Classification in Wireless Capsule Endoscopy with YOLOv8-X
- 统一使用YOLOv8-X模型同时完成检测与分类任务。
- 在验证集上分类准确率达96.10%，mAP为76.8%（IoU=0.5）。
- 基于6345张多样化图像训练，代码与模型开源可复现。
消化道出血是消化系统疾病的关键指征，亟需高效精准的检测方法。本文针对Auto-WCEBleedGen V1挑战赛提出解决方案，获得安慰奖。我们构建了统一的YOLOv8-X模型，用于无线胶囊内镜（WCE）图像中出血区域的检测与分类。通过精心的数据集整理与标注，共使用6,345张多样化图像进行训练，模型在验证集上达到96.10%的分类准确率和76.8%的平均精度均值（[email protected]）。相关实现代码与训练好的模型已公开于https://github.com/pavan98765/Auto-WCEBleedGen。
原文摘要 · Abstract (English)
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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