Abstract: Medical image segmentation is one of the current research hotspots，and the segmentation accuracy significantly impacts the subsequent medical diagnosis. In this paper， we proposed an improved U-Net3+ image segmentation algorithm that incorporated a channel attention mechanism to address the shortcomings of most current medical image segmentation techniques that can not fully utilize and fuse multi-scale feature information. Based on the global jump connection structure of U-Net3+ ，a new channel attention mechanism was designed and embedded into the decoding path of the U-Net3+ network to help the segmentation network adjust the training weights of important information when stitching the global feature map to fuse the global feature information efficiently. Finally，the model was compared and evaluated on two classical medical image segmentation datasets， and the average Dice coefficients reached 74. 31% and 77. 16%，respectively，were 3. 01% and 2. 98% higher than the original U-Net3+ Dice coefficients. The experimental results show that the improved network model effectively improves the segmentation accuracy of medical images.

Key words: U-Net3+, image segmentation, attention mechanism