The identification of brain tumor subregions in medical imaging is a crucial task for accurate diagnosis and treatment planning. Deep learning techniques have shown promise in enhancing the precision of this process in multi-modal magnetic resonance (MR) images. By analyzing various image features and patterns, deep learning algorithms can accurately identify different subregions of a brain tumor in MR images, such as the enhancing core, necrotic core, and peritumoral edema. This approach can provide a more comprehensive understanding of the tumor's characteristics and its impact on the surrounding brain tissue. The ability to identify these subregions can aid clinicians in making more informed decisions about patient care, including surgical planning and radiation therapy.