Objective To address the challenges posed by polyps of varied sizes unclear boundaries lighting effects and their relatively small proportions in images that result in lower segmentation accuracy an improved U-shaped network structure BMR-Net was proposed. Methods The model adopted an encoder-decoder architecture. The encoder partially utilized ResNeSt for feature extraction enhancing the feature extraction performance with only a slight increase in computational cost. Between the encoder and the decoder a boundary prediction generation module BPGM was designed to aggregate high-level features and incorporate a modified spatial pyramid pooling module in which an attention mechanism was introduced. This promoted multi-scale information fusion obtaining a more accurate global feature map representation. For unclear edge areas a reverse attention module was applied to remove previously predicted areas and correct the boundary information. Results Tests were conducted on the CVC-ClinicDB Kvasir-SEG CVC-ColonDB ETIS-Larib and EndoScene datasets with mDice values reaching 0. 930 0. 903 0. 743 0. 712 and 0. 874 respectively. Conclusion This method outperforms other advanced methods in terms of segmentation performance and generalization ability. Furthermore it can segment small-sized polyps more precisely and completely providing early prognosis information for patients with colon polyps.