Research on Blood Cell Segmentation Method Based on Swin-UNet
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.
国家科技期刊平台
NSTL
万方数据
