Detecting brain tumors manually from MRI scans is challenging, time-consuming, and often inaccurate due to similarities in tissue and tumor appearance. This highlights the need for an efficient automatic tumor detection system. We propose a deep learning-based model for brain tumor detection from 2D MRI scans. The model utilizes convolutional neural networks with transformer blocks to enhance spatial and contextual feature recognition. Trained on diverse tumor images, Compared to traditional methods like SVM, our approach showed superior performance. Implemented using TensorFlow and Keras, this method supports accurate and rapid tumor detection for clinical applications. In our study, the CNN model achieved an accuracy of 99.46%, surpassing the current state-of-the-art results. This CNN-based approach can assist doctors in accurately detecting brain tumors in MRI images, potentially significantly speeding up the treatment process.