Summary of Automated Bleeding Detection and Classification in Wireless Capsule Endoscopy with Yolov8-x, by Pavan C Shekar et al.
Automated Bleeding Detection and Classification in Wireless Capsule Endoscopy with YOLOv8-X
by Pavan C Shekar, Vivek Kanhangad, Shishir Maheshwari, T Sunil Kumar
First submitted to arxiv on: 21 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a solution to the Auto-WCEBleedGen Version V1 Challenge, achieving the consolation position. It develops a unified YOLOv8-X model for detecting and classifying bleeding regions in Wireless Capsule Endoscopy (WCE) images. The approach achieves 96.10% classification accuracy and 76.8% mean Average Precision (mAP) at 0.5 IoU on the validation dataset. The paper uses careful dataset curation and annotation to assemble and train a robust model, leveraging 6,345 diverse images. The implementation code and trained models are publicly available. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Gastrointestinal bleeding is an important sign of digestive disorders that needs accurate detection methods. This study shows how to detect bleeding regions in Wireless Capsule Endoscopy (WCE) images using a special kind of AI model called YOLOv8-X. The researchers developed this model to find and identify bleeding areas in WCE images, which is helpful for diagnosing digestive problems. They tested their approach on many images and found it worked well, achieving good accuracy and precision. |
Keywords
» Artificial intelligence » Classification » Mean average precision » Precision
