Brain Tumor Segmentation using 3D UNET

Author(s)	Shiven Singh, Viraj Garg, Sathapriya Loganathan
Country	India
Abstract	To enhance the efficiency of brain tumor diagnosis, we utilized UNet, a CNN architecture, for automatic MRI scan segmentation. Leveraging the BRATS 2018 dataset, which included a series of scans from patients with HGG and LGG. Our approach identifies tumor regions across various MRI sequences by full-volume scans into focused 3D slices. This method offers a faster, consistent alternative to manual segmentation, potentially improving outcomes through more rapid treatment.
Keywords	UNet, MRI scan, Convolutional Neural Network (CNN), HGG, LGG
Field	Medical / Pharmacy
Published In	Volume 5, Issue 6, November-December 2023
Published On	2023-11-25
Cite This	Brain Tumor Segmentation using 3D UNET - Shiven Singh, Viraj Garg, Sathapriya Loganathan - IJFMR Volume 5, Issue 6, November-December 2023. DOI 10.36948/ijfmr.2023.v05i06.8815
DOI	https://doi.org/10.36948/ijfmr.2023.v05i06.8815
Short DOI	https://doi.org/gs632s

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International Journal For Multidisciplinary Research