融合DenseNet121与U-Net，自动识别胃肠道出血区域
ColonNet: A Hybrid Of DenseNet121 And U-NET Model For Detection And Segmentation Of GI Bleeding
- 结合DenseNet121与U-Net，实现病变检测与分割
- 在75支队伍中表现最优，整体准确率达80%
- 适用于真实复杂场景的胃肠道出血分析
本研究提出一种集成深度学习模型，用于从无线胶囊内镜（WCE）视频帧中自动检测和分类胃肠道出血。数据集为MISAHUB团队主办的Auto-WCBleedGen Challenge V2发布。该模型在参与的75支队伍中表现最佳，利用基于CNN的DenseNet与U-Net结构，有效识别真实复杂数据中的出血与非出血区域，整体准确率达到80%，可辅助专业医生进行后续诊断。
原文摘要 · Abstract (English)
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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