MM-UNet: Multi-attention mechanism and multi-scale feature fusion UNet for tumor image segmentation

To address the problems of many parameters and loss of spatial information in traditional Unet networks, this paper proposes a U-Net-based brain tumor segmentation model named MM-UNet to solve the problem of 3D image segmentation. Firstly, the U-Net model performs three times downsampling to extract the image features for the changing characteristics of brain tumor 3D images, which reduces the number of model parameters while maximally preserving the target edge features; then, a structure similar to FPN was used to achieve the fusion of multi-scale predictions; we introduce the channel attention mechanism and pixel attention mechanism to establish the relationship between global features; meanwhile, to improve the generalization ability of the model, data augmentation techniques are used to enhance the information. The experimental results show that the model proposed in this paper has improved the accuracy of brain tumor segmentation compared with U- Net, PSPNet, ICNet, and Fast- SCNN, suggesting 3.9%, 1.3%, 5%, and 3.9%, respectively.