计算机技术与发展2025，Vol.35Issue(10)：62-70,9.DOI:10.20165/j.cnki.ISSN1673-629X.2025.0144
GAM-Net:基于U-Net改进的结肠息肉图像分割算法
GAM-Net:An Improved Colon Polyp Image Segmentation Algorithm Based on U-Net
摘要
Abstract
A colon polyp image segmentation algorithm based on an improved U-Net is proposed to address the issues of unclear boundaries between glands and polyps in colon images and the significant scale differences of polyps that affect segmentation accuracy.Firstly,the original image is enhanced using a global channel spatial attention(GCSA)mechanism to strengthen the representation capability of the deep convolutional neural network.Secondly,an Assem convolution module is embedded after the downsampling stage of the model.This module combines convolution with Transformer methods to effectively learn both global and local feature information of the polyp images.Finally,a multi-scale bitemporal fusion module(MBFM)is used to replace the traditional U-Net channel concatenation method,effectively merging low-level features from the encoder with corresponding high-level features to optimize feature extraction and improve segmentation accuracy.The proposed algorithm is tested on two public polyp segmentation datasets,CVC-ClinicDB and ETIS-ColonDB,and its effectiveness is verified using five evaluation metrics,including MIou and Dice coefficient.Using MIou and Dice as the primary evaluation metrics,on the CVC-ClinicDB dataset,MIou and Dice achieved 83.49%and 89.38%,respectively,showing improvements of 3.01 percentage points and 2.67 percentage points compared to the original method.On the ETIS-ColonDB dataset,these two metrics reached 71.56%and 78.48%,showing improvements of 5.26 percentage points and 2.78 percentage points,respectively.The experimental results demonstrate that the improved U-Net-based algorithm achieves better performance in colon polyp segmentation tasks.关键词
深度学习/息肉图像分割/U-Net/注意力机制/特征融合Key words
deep learning/polyp image segmentation/U-Net/attention mechanism/feature fusion分类
信息技术与安全科学引用本文复制引用
陈亦男,季荣宝,史健婷..GAM-Net:基于U-Net改进的结肠息肉图像分割算法[J].计算机技术与发展,2025,35(10):62-70,9.基金项目
2023年度黑龙江省属高校基本科研业务费项目-应用研发计划重点领域专项项目(2023-KYYWF-0547) （2023-KYYWF-0547）
