Tumor segmentation algorithm in mammograms based on improved YOLOv8
ZHANG Lingyu
CONG Jinyu
LI Xiang
WANG Pingping
LIU Kunmeng
SI Xingyong
WEI Benzheng
Abstract：To improve the segmentation accuracy and efficiency of breast tumors under limited computing resources,we proposed a tumor segmentation algorithm for mammograms by improving the YOLOv8 model.Firstly,a feature extraction module PC-C3K2 was de-signed by pinwheel convolution(PConv)and C3K2 module to significantly expand the receptive field and reduce the number of model parameters.Secondly,the spatial channel decoupling downsampling module was utilized for downsampling,and the computational com-plexity was reduced by independently processing the spatial information and channel information.The experimental results on the IN-breast and the CBIS-DDSM datasets showed that the mAP50 value of the proposed algorithm reached 91.8%,and the number of param-eters could be reduced by 28%.The algorithm can significantly reduce the number of model parameters while improving the tumor seg-mentation accuracy of mammograms,which is of great significance for the early screening and diagnosis of breast tumors.
Keywords：YOLOv8Tumor segmentationLightweightBreast tumorMammograms
Publication Date：2025-06-30
Online Publishing Date：2026-09-11(First online date of this platform, not the publication date of the document)
Pages：8( 135-142 )