现代信息科技2024，Vol.8Issue(5)：124-128,5.DOI:10.19850/j.cnki.2096-4706.2024.05.027
基于Swin-UNet血细胞分割方法研究
Research on Blood Cell Segmentation Method Based on Swin-UNet
摘要
Abstract
The result of blood cell segmentation is an important basis for doctors to diagnose patient's condition.Medical blood cell detection methods are susceptible to external interference and have low efficiency.Traditional image segmentation models have low accuracy and poor segmentation performance for blood cell images with cluttered backgrounds.To improve the efficiency and accuracy of blood cell segmentation,an improved blood cell segmentation algorithm based on Swin-UNet is proposed.Firstly,Swin-UNe is introduced through transfer learning to pre train model parameters on ImageNet as the feature extraction front-end,improving the model's generalization ability.Secondly,based on the Swin-UNet algorithm,the normalization function of the down-sampling module is improved to improve the training speed of the model.The experimental results show that the proposed method has significant improvements in accuracy,recall,and F1 index,with values of 97%,98%,and 97%,respectively,which is 3%higher than the traditional UNet segmentation method.关键词
血细胞分割/图像分割/深度学习Key words
blood cell segmentation/image segmentation/Deep Learning分类
信息技术与安全科学引用本文复制引用
邬云熙,杨伏洲,杨尧,刘承前..基于Swin-UNet血细胞分割方法研究[J].现代信息科技,2024,8(5):124-128,5.
