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The brain tumor segmentation task has attracted considerable attention in the realm of medical image segmentation, necessitating scholars to employ efficient deep learning techniques for precise handling given its inherent complexity and diversity. With the rapid advancement of deep learning technologies, a plethora of models tailored for the brain tumor segmentation challenge (BraTS) dataset has emerged. This paper presents a comprehensive review of three deep learning approaches for brain tumor segmentation, focusing on the BraTS dataset. Firstly, the paper meticulously introduces the background and origin of the BraTS dataset, thoroughly dissecting the structural composition and various performance evaluation metrics of this multimodal dataset. This provides a theoretical foundation for subsequent in-depth analyses of deep models. Secondly, the paper conducts an in-depth discussion regarding the performance of three distinct deep learning models on the BraTS dataset. This includes convolutional neural network (CNN), U-Net convolutional networks (U-Net), and Transformer models, elucidating the optimizations and enhancements made to their foundational models in the domain of brain tumor segmentation. The paper further delves into the strategies employed by these models to address challenges such as dataset class imbalance and issues related to modeling, feature extraction, and feature fusion. Lastly, the paper summarizes the current trends in model research and forecasts the future direction of Transformer models, emphasizing that, in addition to improving model performance, future research will focus on self-supervised learning and lightweight methodologies. The paper aims to provide an in-depth understanding and inspiration for researchers who are initially exploring this field, offering valuable insights for the development of higher-performing and more generalizable methods for brain tumor segmentation.