Transformer-Based Wireless Capsule Endoscopy Bleeding Tissue Detection and Classification

• URL: http://arxiv.org/abs/2412.19218v1
• Date: Thu, 26 Dec 2024 13:49:39 GMT
• Title: Transformer-Based Wireless Capsule Endoscopy Bleeding Tissue Detection and Classification
• Authors: Basit Alawode, Shibani Hamza, Adarsh Ghimire, Divya Velayudhan,
• Abstract summary: We design an end-to-end trainable model for the automatic detection and classification of bleeding and non-bleeding frames. 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.
• Score: 0.562479170374811
• License:
• Abstract: 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.

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