Medical image segmentation is important for medical diagnosis, and deep neural networks (CNN) have made significant progress in this area. However, CNN have the disadvantage of focusing mainly on localized features. In contrast, the Transformer architecture is able to consider the entire input sequence and therefore captures the global contextual information of medical images more efficiently. In this study, we propose an innovative approach to enhance image detail information by first pre-processing using Contrast Constrained Adaptive Histogram Equalization (CLAHE), and then combining U-Net with the VIT Transformer framework to further process MRI brain tumor medical images. The method proposed in this study is improved VIT based U-Net. This study achieved particularly outstanding results in the recognized dataset Brats2020, and the evaluation metrics results of the algorithm proposed in this study are more than 99%, and the results in the MSD dataset, although slightly inferior to the Brats2020 dataset, are still advantageous in comparison with other algorithms.