ColonNet: A Hybrid Of DenseNet121 And U-NET Model For Detection And Segmentation Of GI Bleeding

Ayushman Singh, Sharad Prakash, Aniket Das, Nidhi Kushwaha
12/6/2024
eess.IVcs.CVcs.LG

No official code found. Best implementation: SINISTERX69/ColonNet (3 stars, Apache-2.0, last commit 2024-12-10).

Abstract

This study presents an integrated deep learning model for automatic detection and classification of Gastrointestinal bleeding in the frames extracted from Wireless Capsule Endoscopy (WCE) videos. The dataset has been released as part of Auto-WCBleedGen Challenge Version V2 hosted by the MISAHUB team. Our model attained the highest performance among 75 teams that took part in this competition. It aims to efficiently utilizes CNN based model i.e. DenseNet and UNet to detect and segment bleeding and non-bleeding areas in the real-world complex dataset. The model achieves an impressive overall accuracy of 80% which would surely help a skilled doctor to carry out further diagnostics.

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Code Implementations(1)

SINISTERX69/ColonNetCommunity100%
Apache-2.0

A Deep Learning Model that detects and highlights the instances of bleeding in the small intestine. Submission for WCEBleedGen Ver2.

33PythonFeb 24, 20241 years agoApache-2.0

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Cite this paper

@article{singh2024colonnet,
  title  = {ColonNet: A Hybrid Of DenseNet121 And U-NET Model For Detection And Segmentation Of GI Bleeding},
  author = {Ayushman Singh and Sharad Prakash and Aniket Das and Nidhi Kushwaha},
  year   = {2024},
  eprint = {2412.05216},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2412.05216v1}
}

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