Brain Tumor Segmentation Based on Deep Learning Using Multimodal MRI Images

Accurate segmentation and localization of brain tumors from magnetic resonance imaging (MRI) scans remain one of the major and ongoing challenges in the field of medical image analysis. This task is critically important, as it directly impacts clinical diagnosis, surgical planning, and treatment strategies for patients. The heterogeneous nature of brain tumors, along with their varying sizes, shapes, and locations, makes automated segmentation particularly complex. To address this, recent advanced methodologies commonly incorporate multiple MRI modalities—such as T1-weighted, contrast-enhanced T1 (T1c), T2-weighted, and FLAIR images—each of which offers complementary information regarding different tissue characteristics. These multi-modal approaches have significantly improved segmentation performance by providing a more comprehensive understanding of the tumor’s structure. However, despite the promising results achieved on benchmark datasets like BRATS 2018, many state-of-the-art methods rely on deep architectures with high computational complexity, which can hinder their deployment in real-time or resource-constrained clinical environments. To overcome these limitations, this study introduces a novel deep learning-based framework tailored specifically for brain tumor segmentation. Extensive experiments conducted on the BRATS 2018 dataset reveal that the proposed approach not only surpasses existing models in terms of accuracy but also shows strong generalization capability and robustness when segmenting complex and irregular tumor boundaries, making it a promising tool for real-world clinical applications.