Deep Learning Approaches for Brain Tumor Detection and Segmentation in MRI Imaging

Abstract

The identification and delineation of brain tumors are essential for precise diagnosis, treatment planning, and improved patient outcomes. MRI has emerged as the preferred imaging method, offering high-resolution scans with detailed brain tissue differentiation. Recent strides in deep learning have significantly enhanced the automation of brain tumor detection and segmentation, diminishing the need for manual analysis. This review examines state-of-the-art deep learning techniques for brain tumor detection and segmentation in MRI, emphasizing architectures such as CNNs, U-Net, and advanced models incorporating GANs. The study explores the integration of these models with various MRI modalities, including T1-weighted, T2-weighted, FLAIR, and contrast-enhanced MRI, to achieve greater precision in tumor boundary and type identification. Furthermore, the paper addresses challenges like data heterogeneity, model interpretability, and computational requirements, alongside recent advancements in data augmentation and model explain ability. This research underscores the potential of deep learning to streamline clinical workflows and support radiologists in early and accurate brain tumor diagnosis, while also considering future directions for enhancing robustness and clinical applicability.