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.

翻译：本研究提出一种集成深度学习模型，用于在无线胶囊内窥镜视频帧中自动检测和分类胃肠道出血。数据集由MISAHUB团队主办的Auto-WCBleedGen挑战赛V2版本发布。我们的模型在参与该竞赛的75支队伍中取得了最佳性能。该模型旨在高效利用基于CNN的DenseNet与UNet架构，在真实世界复杂数据集中检测并分割出血区域与非出血区域。模型实现了80%的整体准确率，这将有效辅助专业医生进行后续诊断。