ColonNet: A Hybrid Of DenseNet121 And U-NET Model For Detection And Segmentation Of GI Bleeding
This work addresses the need for efficient diagnostic tools in medical imaging for gastrointestinal bleeding, though it is incremental as it combines existing methods.
The study tackled the problem of automatic detection and segmentation of gastrointestinal bleeding in wireless capsule endoscopy videos by developing a hybrid deep learning model, which achieved the highest performance in a competition with 75 teams and an overall accuracy of 80%.
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.