Due to the varying sizes and blurry boundaries of polyp regions in colonoscopy images the segmentation accuracy of existing models is not satisfactory To overcome this challenge a new approach called

Colonoscopy is an essential procedure for detecting and treating abnormalities in the colon, including polyps. However, accurately identifying and segmenting polyp regions in colonoscopy images remains a challenge due to their varying sizes and blurry boundaries. Existing models fail to provide satisfactory segmentation accuracy, prompting the development of a new approach called Progressive Reduction Network (PRNet).

PRNet aims to locate and refine polyp regions step by step, using Res2Net to extract features of the polyp region. The extracted features are then combined with a multi-scale cross-level fusion module to identify the exact location of the polyp. This module enhances the network's ability to extract features from multi-scale polyp regions and thus improves the segmentation accuracy.

Furthermore, PRNet utilizes a module for filtering features to remove redundant and interfering data from the encoded features. An uncertain region processing module is also included to enhance edge detail feature recognition, progressively decreasing thresholds from top to bottom. Finally, a multi-scale context-aware module is designed to further explore potential semantic feature information.

The experimental results on the Kvasir-SEG, CVC-Clinic, and ETIS datasets show that PRNet achieves Dice coefficients of 92.09%, 93.05%, and 74.19%, respectively. PRNet demonstrates significant improvements in segmentation accuracy for cases with multi-scale lesion areas and fuzzy lesion boundaries.

Overall, PRNet is a promising approach for accurately identifying and segmenting polyp regions in colonoscopy images. Its multi-scale cross-level fusion module and other features allow for improved feature extraction and better segmentation accuracy. This development could lead to better detection and treatment of abnormalities in the colon, ultimately improving patient outcomes