Development of an Advanced Machine Learning Model for 3D Brain Segmentation in MRI

Abstract

In the last few years, the rates of occurrence and death associated with brain tumors have shown a worrisome increase and now appear to be on a sustained uptick. The most recent report from the American Cancer Society (ACS) estimated that in 2020, 24,531 people in the United States would receive a diagnosis of a brain or other central nervous system tumor, and 18,306 would die from one of those tumors. The current research effort is a penetration into the brain tumor segmentation problem, and it seems to kick off with a quite elaborate preamble. To solve the problems of the heterogeneous tumors, the inconsistent morphology, and the deployment of clinical models, we propose a revised model of 3D Residual U-Net. This model has incorporated Convolutional Block Attention Modules (CBAM) a type of neural network component that allows it to better understand which pixels to pay attention to when outputting its results. We trained and evaluated this model on the BraTS benchmark 2020 dataset. The suggested framework attains high segmentation accuracy, along with computational efficiency, amassing Dice scores of 68% for ET, 83% for TC, and 91% for WT, strong generalization to the diverse, complex morphologies of brain tumors being evident here. By amending U-Net with residual learning, attention mechanisms, and a composite loss function (Dice + Focal), this framework provides a clinically viable approach that bridges the gap between research and real-world applicability in neuro- oncological imaging.