Brain tumor segmentation from Magnetic Resonance Imaging (MRI) is an important task in computer-aided diagnosis because accurate identification of tumor regions supports clinical assessment and treatment planning. However, the complex structure, irregular shape, intensity variation, and heterogeneous appearance of brain tumors make automated segmentation challenging. This study presents a comparative deep learning framework for brain tumor segmentation using the BraTS 2020 dataset and two-dimensional (2D) MRI images. In this study, we explore 2D deep learning architectures for automated tumor segmentation, focusing on U-Net, Vision Transformer (ViT), and Res-ViT models. U-Net, with its encoder–decoder design and skip connections, has been widely adopted for medical image segmentation due to its ability to capture fine-grained spatial features. ViT, leveraging self-attention mechanisms, introduces a global receptive field that enhances contextual understanding across slices. The Res-ViT hybrid combines residual learning with transformer-based attention, aiming to balance local feature extraction and long-range dependency modelling. Preprocessing steps, including skull stripping, intensity normalisation, and bias field correction, were applied to ensure consistency across scans. Data augmentation techniques such as rotation, flipping, and elastic deformation were employed to mitigate overfitting and improve generalisation. The models are evaluated using important segmentation metrics, including Intersection over Union (IoU), accuracy, precision, recall/sensitivity, loss, and 95th-percentile Hausdorff Distance (HD95). The comparative analysis aims to identify the strengths and limitations of convolutional and transformer-based approaches for 2D brain tumor segmentation. The study demonstrates the potential of combining local feature extraction and global contextual learning to achieve more accurate and robust brain tumor segmentation from multimodal MRI images.