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

Pavan C Shekar, Vivek Kanhangad, Shishir Maheshwari, T Sunil Kumar
12/21/2024
cs.CVcs.AI

Official code: pavan98765/Auto-WCEBleedGen (2 stars, last commit 2024-12-24).

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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Code Implementations(1)

pavan98765/Auto-WCEBleedGenOfficial100%
22MATLAB, PythonSep 24, 20231 years ago

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Cite this paper

@article{shekar2024automated,
  title  = {Automated Bleeding Detection and Classification in Wireless Capsule Endoscopy with YOLOv8-X},
  author = {Pavan C Shekar and Vivek Kanhangad and Shishir Maheshwari and T Sunil Kumar},
  year   = {2024},
  eprint = {2412.16624},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2412.16624v1}
}

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