## 1 Introduction
Researchers have found that Vision Transformers cannot effectively capture local semantic information, while convolutional neural networks have limitations in capturing global semantic information. The trend in the field of brain-tumor segmentation is to deeply integrate the two, such as the work by [5] proposing the TransBTS network, which combines Transformers for the first time in 3D MRI brain-tumor segmentation, allowing it to capture both global and local features simultaneously. The encoder first utilizes a 3D CNN to extract volumetric spatial feature maps, then models global features using a transformer, enabling the comprehensive capturing of global and local features in the image. However, due to the quadratic computational complexity and sequence length of the Transformer model, TransBTS requires higher computational resources when dealing with large-scale or high-resolution images. The model also has a higher number of parameters, and since the Transformer does not adopt a hierarchical architecture, it has a certain impact on segmentation accuracy. In conclusion, the current brain-tumor segmentation methods that combine CNN and Transformer are able to capture both global and local features simultaneously. However, how to optimally integrate these two types of features to improve algorithm segmentation accuracy and efficiency remains a challenge. In view of this, we proposed a glioma segmentation framework based on a dual-path encoder network and multi-view dynamic fusion model. The main contributions of this model are as follows:  
1. An innovatively proposed dual-path encoder architecture based on CNN and Swin Transformer, combined with a CNN and an improved Swin Transformer, a convolution operation is used to extract local dependencies and rich local features, and an improved Swin Transformer is used to learn global dependencies for global modeling. Then, feature fusion and upsampling are carried out to produce the segmentation results. Deeply integrating CNN and Transformer, leveraging the strengths of both frameworks, effectively enhances the accuracy of brain-tumor boundary recognition. Finally, considering that as the depth of the Swin Transformer model increases, the differences in the amplitudes of cross-layer activations significantly grow, mainly due to the outputs of residual units directly added to the main branch. This instability issue in large-scale models of the Swin Transformer is addressed by normalizing the activation values of each residual branch and merging them back to the main branch, thereby enhancing the stability of training through structural improvements. 2. A new location coding module is proposed. By adding a trainable parameter in the local window (M \(\times\) M \(\times\) M) and integrating location information in self-attention training, the Swin Transformer encoder structure can obtain rich location information, which helps to improve the segmentation accuracy of the brain-tumor model, especially for the recognition of the brain-tumor boundary region. M represents the local window size during Swin Transformer training, and r represents the relative positional offset. 3. For validation of the benchmark dataset, this study utilized publicly available datasets named Brats 2021 [6; 7; 8] and Brats 2019 [9], which were provided by the organizers of MICCAI (International Conference on Medical Image Computing and Computer-Assisted Intervention) and served as part of the BraTS challenge. The experimental results of the Brats 2021 and Brats 2019 datasets demonstrated the effectiveness of the model, further improving the segmentation accuracy of brain tumors.