自动化与信息工程2025，Vol.46Issue(4)：28-34,7.DOI:10.12475/aie.20250404
改进U-Net的X射线乳腺肿瘤区域分割方法
X-ray Breast Tumor Region Segmentation Method Using an Improved U-Net
摘要
Abstract
Breast cancer is a serious disease that threatens women's health,and timely and accurate diagnosis is crucial for reducing its mortality rate.To improve the accuracy of breast cancer diagnosis,this study proposes an enhanced U-Net-based method for segmenting breast tumor regions in X-ray images.First,a parallel attention module is constructed by integrating spatial and channel attention mechanisms to strengthen the network's ability to extract and identify key features.Next,the parallel attention module is combined with a residual module to design a residual parallel attention module,enhancing the U-Net model's deep feature extraction and high-efficiency focusing capabilities.Finally,the residual parallel attention module is incorporated into the encoder part of the U-Net model,improving its segmentation accuracy for X-ray breast tumor regions.Experimental results on the CBIS-DDSM dataset demonstrate that the improved U-Net model achieves Dice coefficients and mean intersection over union(mIoU)of 94.20%and 90.76%,respectively,significantly enhancing the segmentation accuracy of X-ray breast tumor regions.关键词
图像分割/注意力机制/乳腺肿瘤/残差网络/U-NetKey words
image segmentation/attention mechanism/breast tumor/residual network/U-Net分类
信息技术与安全科学引用本文复制引用
陈翔,蔡延光,龚国俊,张瑞湖,蔡颢..改进U-Net的X射线乳腺肿瘤区域分割方法[J].自动化与信息工程,2025,46(4):28-34,7.基金项目
广东省科技计划项目(2016A050502060,2020B-1010010005) （2016A050502060,2020B-1010010005）
广州市科技计划项目(202206010011,2023B-03J1339). （202206010011,2023B-03J1339）
