VGG16U-Net with Attention Based Semantic Segmentation of Gastrointestinal Abnormalities

The human gastrointestinal (GI) tract is sus-ceptible to a myriad of diseases that can profoundly impact health. Therefore, timely detection and intervention are critical in halting the progression of these diseases and preventing their potential transformation into cancer. Regular screenings, diagnostic tests, and early symptom recognition play vital roles in ensuring early intervention and better patient outcomes in managing GI-related conditions. In recent years, scientists have increasingly turned to advanced technologies, particularly deep learning algorithms, to revolutionize the detection and segmentation of colorectal anomalies. Leveraging the power of artificial intelligence, researchers are exploring sophisticated deep learning models capable of analyzing vast amounts of endoscopic imagery with remarkable precision and efficiency. In our proposed paper, we introduce an approach to the automated segmentation of colorectal anomalies, through the development of an end-to-end architecture named VGGI6U-Net. The architecture enhances the capability of the framework for precise segmentation By leveraging the features of VGG 16 and integrating an attention mechanism into the U-Net framework. The model exhibits promising performance in accurately identifying and delineating polyps and bleeding regions within images. The incorporation of the attention mechanism enables the network to focus on salient features, thereby further improving segmentation accuracy and reducing false positives.