## 1 Introduction
### Literature Review
Motivated by the powerful long-range information interaction of Vision Transformer, their model leverages self-supervised pre-training to achieve advanced segmentation performance, and they obtained a dice similarity coefficient that does not exceed 86.4%. Furthermore, [PERSON] et al. [24] proposed a YOLO-U-Net architecture combining CNN and FCN methodologies for brain tumor detection and segmentation. By integrating deep learning techniques, their approach provides automatic tumor detection and segmentation with high accuracy and precise localization. A high accuracy reached 97%, with 277 images for training, 69 for validation, and 14 for tests. Recent advancements in brain tumor segmentation algorithms have leveraged the U-Net architecture, integrating novel modules to improve performance. For instance, [PERSON] et al. [25] introduced a U-Net and Transformer-based model with a hybrid attention mechanism, achieving high segmentation accuracy across various datasets. The best achievement in BraTS2021 reported a dice coefficient of 95%, with a sample size of 1480 patients studied. Similarly, [PERSON] et al. [26] proposed a DO-U-Net model incorporatingresidual modules, multi-scale feature fusion, and attention mechanisms to enhance segmentation accuracy while reducing computational complexity. Furthermore, lightweight architectures like Yaru3 DFPN and TFFbU have been developed to address the computational demands of traditional CNNs while maintaining segmentation performance [26]. These models integrate feature pyramid networks and attention mechanisms to achieve accurate and efficient segmentation, promising clinical applicability; their dice coefficients did not exceed 86.2%. Moreover, hybrid models combining deep learning with meta-heuristic algorithms, as demonstrated by [PERSON] et al. [27], showcase improved segmentation accuracy and computational efficiency. By integrating meta-heuristic optimization with U-Net architecture, these models enhance feature extraction and classification, facilitating precise tumor delineation, but no information about their results as numbers. In addition to architectural innovations, researchers have explored integrating advanced techniques such as LSTM [28] and attention mechanisms [29] into U-Net-based models, and good results in dice coefficients have been achieved. Still, their method suffered from high-time computation. These enhancements enable the capture of temporal dependencies and contextual information, leading to more accurate tumor segmentation. [PERSON] and his colleague [30] presented a serial encoding-decoding structure that improves segmentation performance by combining hybrid dilation convolution (HDC) units and concatenating every unit of two serial networks. They also suggest a novel loss function to help the model focus on challenging to segment and categorize samples. [PERSON] et al. [31] suggested a two-stage strategy. In the initial stage, an encoder-decoder-oriented U-Net with a residual network is deployed to detect various brain cancers. In the second stage, a YOLO2 (you only look once)-based transfer learning strategy is utilized to identify the obtained tumors. Utilizing the suggested Enhanced Invasive Bat (IIB)-based Deep Residual network model, [PERSON] and [PERSON] [32] developed an effective brain tumor detection method. As a result, the enhanced Invasive Weed Optimization (IWO) and Bat Algorithm (BA) are incorporated into the proposed IIB algorithm. [PERSON] et al. [33] presented Generative Adversarial Networks (GANs) with the ability to synthesize realistic images to enhance the training of deep learning models. Additionally, they investigated the CyclaGAN architecture through hyperparameter adjustments and carried out brain tumor image segmentation. Encoder-decoder-based convolutional neural networks were proposed by [PERSON] and [PERSON] [34] as an approach to automate brain tumor segmentation fully. A new deep-learning model for medical image segmentation called nn-U-Net, was proposed by [PERSON] [35].