3D U-Net for Brain Tumor Detection and Segmentation

Authors

  • P Swathi  ECE Department, NBKR Institute of Science and Technology, Vidyanagar, Andhra Pradesh, India
  • M Narayana Ramakrishna  ECE Department, NBKR Institute of Science and Technology, Vidyanagar, Andhra Pradesh, India
  • K Vilasini  ECE Department, NBKR Institute of Science and Technology, Vidyanagar, Andhra Pradesh, India
  • M Sai Hemanth  ECE Department, NBKR Institute of Science and Technology, Vidyanagar, Andhra Pradesh, India
  • J Nirmal Sumanth  ECE Department, NBKR Institute of Science and Technology, Vidyanagar, Andhra Pradesh, India

Keywords:

Convolutional Neural Network, U-Net architecture, 3D volumes, Brain Tumor (Gliomas), Segmentation.

Abstract

We describe a fully automated brain tumor segmentation approach based on Convolutional Neural Network in this paper. The suggested network takes the 3D Flair Magnetic Resonance Image (MRI) of glioblastomas as input. These tumors can appear anywhere in the brain and have practically any shape or size by their very nature. These factors compel us to investigate an artificial intelligence system that takes advantage of a flexible, high-capacity neural network while remaining incredibly efficient. We describe the U-Net model that we've found to be important for achieving effective performance in segmenting the tumor in brain and the stage of the patient.

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International Journal of Scientific Research in Science and Technology

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Published

2022-06-30

Issue

Volume 9, Issue 3, May-June-2022

Section

Research Articles

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Copyright (c) IJSRST

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
P Swathi, M Narayana Ramakrishna, K Vilasini, M Sai Hemanth, J Nirmal Sumanth "3D U-Net for Brain Tumor Detection and Segmentation" International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 9, Issue 3, pp.415-421, May-June-2022.