Enhancing brain tumor segmentation using attention based convolutional UNet on MRI images.

ABSTRACT: Precise segmentation of brain tumors is essential for efficient diagnosis and therapy planning. While current automated methods frequently fail to capture complicated tumor shapes, traditional manual methods are laborious, subjective, and unpredictable. These issues are addressed by the suggested Attention-based Convolutional U-Net (ACU-Net) model, which incorporates attention processes into the U-Net architecture. The objective is to enhance the degree of precision and dependability of the tumor's edge delineation by proposing and testing the ACU-Net model-based brain tumor segmentation on MRI data. The research framework consists of data acquisition from the BraTS 2018 MRI data set. The first processing steps carried out in this study were the normalization of acquired data, spatial reso

SUBMITTER: Abrar M 

PROVIDER: S-EPMC12541010 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature