An improved U-Net model(channel attention module U-Net,CAMU-Net)is proposed to achieve precise segmentation of retinal vessels.CAMU-Net model enhances its understanding of regional features by employing residual enhancement convolution to extract important information from the regions,improves the global feature acquisition capability by introducing feature refinement module to promote feature extraction,realizes precise segmentation by adding channel attention module to capture image features accurately,and enhances its capability to perceive target boundaries and details through a multi-scale feature fusion structure.The ablation study on the DRIVE dataset validates the role of each module in retinal vessel segmentation.The comparison with other mainstream network models on DRIVE and STARE datasets verify that CAMU-Net model is superior to other models.
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