Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images.

ABSTRACT: Brain tumor localization and segmentation from magnetic resonance imaging (MRI) are hard and important tasks for several applications in the field of medical analysis. As each brain imaging modality gives unique and key details related to each part of the tumor, many recent approaches used four modalities T1, T1c, T2, and FLAIR. Although many of them obtained a promising segmentation result on the BRATS 2018 dataset, they suffer from a complex structure that needs more time to train and test. So, in this paper, to obtain a flexible and effective brain tumor segmentation system, first, we propose a preprocessing approach to work only on a small part of the image rather than the whole part of the image. This method leads to a decrease in computing time and overcomes the overfitting problems

SUBMITTER: Ranjbarzadeh R 

PROVIDER: S-EPMC8149837 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature