Bleeding Segmentation Based on a U-Formed Network with Separable Contextual Feature-Guided in Wireless Capsule Endoscopy Images

A revolutionary imaging device termed wireless capsule endoscopy (WCE) provides a painless, noninvasive vision of the complete gastrointestinal (GI) tract. Given the poor intensities and vague differences between bleeding and normal regions, successful bleeding region detection in WCE recordings is essential for the premature diagnosis and restoration of intestinal cancer. Conversely, in this research, we propose a flexible segmentation map construction technique centered on U-shaped architecture and supervised by the suggested Separa-ble Contextual Feature-Guided (SCFG) module to identify GI bleeding. The SCFG module arrives at every encoder-decoder block for guiding the model in accurately recognizing various bleeding zones. The primary drawback is that the module can be improved by better characterizing the bleeding zone due to the usage of numerous alternative convolutional forms. Additionally, a bottleneck module is proposed, serving as a feature transition module. The results of the studies reveal that our suggested system outperforms state-of-the-art segmentation techniques by a margin of 0.47 % in the dice coefficient. The dice score's calculated segmentation rate is 91.14 %, while the intersection over union (IoU) segmentation efficiency is 83.72 %.