# New Approach for Brain Tumor Segmentation Based on Gabor Convolution and Attention Mechanism
## 1 Introduction
Many researchers have used it as the basic framework for deeper research. For example, [PERSON] et al. [9] designed a symmetric differential image-based FCN network for improved segmentation accuracy, which is characterized by three up-sampling structures for feature extraction. Subsequently, [PERSON] et al. [10] proposed the U-Net architecture based on the FCN network; the U-Net utilizes a training strategy that combines the features of the encoding path and those of the decoding path through long connections to reduce information loss. In addition, the U-Net network uses methods such as data augmentation and discard regularization during training to improve the generalization ability and robustness of the network. It has been verified that U-Net has achieved excellent success in the field of medical images and also has a great impact on brain tumor medical segmentation. For example, [PERSON] et al. [11] introduced the cavity convolution structure into the U-Net architecture for improved segmentation accuracy; the cavity convolution can control the receptive field without changing the size of the feature map, and the multi-scale information can thus be accurately extracted. [PERSON] et al. [12] added the attention mechanism into the U-Net architecture for brain tumor segmentation, the improved performance of this algorithm can be ascribed to a dense residual block utilized to replace the double convolution structure and the attention mechanism incorporated into decoding. [PERSON] et al. [13] also added the attention mechanism into the U-Net model; the difference is that it incorporates the attention module into the jump connectivity path of U-Net, which consists of a spatial module and an attention module. The attention model is capable of modeling both spatial and channel attention. It leads to higher segmentation accuracy with reduced memory cost. [PERSON] et al. [14], on the other hand, incorporated the attention mechanism into the structure of the Res-U-Net [15] architecture and added a bootstrap decoder. As a result, more accurate features can be obtained and the segmentation accuracy is thus improved. The 3D Convolutional Block Attention Module (3D CBAM) is an advanced attention mechanism designed to enhance the performance of 3D convolutional neural networks. [PERSON] et al. [16] illustrated the effectiveness of 3D CBAM in brain tumor segmentation by incorporating it into a U-Net framework, achieving significant improvements in accuracy due to enhanced feature extraction from multimodal MRI images. [PERSON] [17] et al. proposed a new model for brain tumor segmentation. One of the most important features of the model is that the segmentation is performed in multiple stages. Firstly, an initial network is used to generate a region that serves to constrain the tumor site that is about to be segmented. Immediately after that, a 3D U-Net network that also contains an attention mechanism is used to perform a multiregional segmentation of the constrained region. Finally, in order to solve the multi-segmentation problem in a better way, a new loss function is developed to refine the network. The work by [PERSON] [18] et al. also incorporates multiple stages into the network for segmentation. A two-stage cascaded U-Net is designed for brain tumor segmentation, where rough results obtained in the first stage are refined in the second stage to obtain segmentation results with improved accuracy. Although many of the above methods have achieved excellent results for brain tumor segmentation, their performance on some tumor images remains unsatisfactory. In practice, accurate segmentation of some tumor images is difficult due to the various distributions of gray values and complex local textures in these images. In this paper, a new method is proposed for brain tumor segmentation. The proposed method performs segmentation of multimodal brain tumor MRI images with a new deep learning model. Specifically, based on the 3D U-Net architecture, the Gabor convolution is incorporated into the encoder.