Exploring Deep Learning Techniques for MRI Brain Tumor Image Segmentation: A Survey

Medical image segmentation is critical for accurate brain tumor Diagnosis as well as treatment. MRI is the most popular technique for visualizing brain structures and abnormalities, such as tumors. This abstract describes thedeep learningtechniques for dividing the brain tumors in MRI images, with the ultimate goalof improving diagnostic efficiency and precision. This survey paper comprehensively reviews and analyzes various image segmentation methods applied to MRI brain tumor detection, with a focus on highlighting recent advancements and challenges in the field. The survey categorizes existing MRI brain tumor segmentation methods into several key groups, including conventional image processing techniques,modern and traditional deep learning (DL) methods. For each category, we delve into the underlying principles, methodologies, and notable algorithms, providing an in-depth understanding of their strengths and limitations. Additionally, we cover in this survey the standard evaluation measures that happens typically accustomed to evaluate the success rate of techniques for segmentation, as well as publicly available MRI brain tumor datasets.