Using radio signals from a magnetic field, magnetic resonance imaging (MRI) represents a medical procedure that produces images to provide more information than typical scans. Diagnosing brain tumors from MRI is difficult because of the wide range of tumor shapes, areas, and visual features, thus universal and automated system to handle this task is required. Among the best deep learning methods, the U-Net architecture is the most widely used in diagnostic medical images. Therefore, U-Net-based attention is the most effective automated model in medical image segmentation dealing with various modalities. The self-attention structures that are used in the U-Net design allow for fast global preparation and better feature visualization. This research aims to study the progress of U-Net design and show how it improves the performance of brain tumor segmentation. We have investigated three U-Net designs (standard U-Net, Attention U-Net, and self-attention U-Net) for five epochs to find the last segmentation. An MRI image dataset that includes 3064 images from the Kaggle website is used to give a more comprehensive overview. Also, we offer a comparison with several studies that are based on U-Net structures to illustrate the evolution of this network from an accuracy standpoint. U-Net-based self-attention has demonstrated superior performance compared to other studies because self-attention can enhance segmentation quality, particularly for unclear structures, by concentrating on the most significant parts. Four main metrics are applied with a loss function of 5.03 %, a validation loss function of 4.82 %, a validation accuracy of 98.49 %, and an accuracy of 98.45 %.