Gliomas are known to have different sub-regions within the tumor, including the edema, necrotic, and
active tumor regions. Segmenting of these regions is very important for glioma treatment decisions
and management. This paper aims to demonstrate the application of U-Net and pre-trained U-Net
backbone networks in glioma semantic segmentation, utilizing different magnetic resonance imaging
(MRI) image weights. The data used in this study for network training, validation, and testing is the
Multimodal Brain Tumor Segmentation (BraTS) 2021 challenge. In this study, we applied the U-Net
and different pre-trained Backbone U-Net for the semantic segmentation of glioma regions. The
ResNet,…
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