# Transformer-Based Wireless Capsule Endoscopy Bleeding Tissue Detection and Classification

arXiv: https://arxiv.org/abs/2412.19218 · HTML: https://mlsanity.com/paper/2412.19218/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 10 references that are in the map.
- First posted on arXiv 26 Dec 2024.

## Built on by (most cited first, 1 of 1)

- [Divide-then-Diagnose: Weaving Clinician-Inspired Contexts for Ultra-Long Capsule Endoscopy Videos](https://mlsanity.com/paper/2604.21814/) (2026)

## Abstract (arXiv)

Informed by the success of the transformer model in various computer vision tasks, we design an end-to-end trainable model for the automatic detection and classification of bleeding and non-bleeding frames extracted from Wireless Capsule Endoscopy (WCE) videos. Based on the DETR model, our model uses the Resnet50 for feature extraction, the transformer encoder-decoder for bleeding and non-bleeding region detection, and a feedforward neural network for classification. Trained in an end-to-end approach on the Auto-WCEBleedGen Version 1 challenge training set, our model performs both detection and classification tasks as a single unit. Our model achieves an accuracy, recall, and F1-score classification percentage score of 98.28, 96.79, and 98.37 respectively, on the Auto-WCEBleedGen version 1 validation set. Further, we record an average precision (AP @ 0.5), mean-average precision (mAP) of 0.7447 and 0.7328 detection results. This earned us a 3rd place position in the challenge. Our code is publicly available via https://github.com/BasitAlawode/WCEBleedGen.