Development of brain tumor segmentation of magnetic resonance imaging (MRI) using U-Net deep learning

Authors

DOI:

https://doi.org/10.15587/1729-4061.2021.238957

Keywords:

Magnetic Resonance Imaging (MRI), deep learning, Convolutional Neural Network (CNN), 3D U-Net architecture, brain tumors, segmentations

Abstract

Brain tumors are the growth of abnormal cells or a mass in a brain. Numerous kinds of brain tumors were discovered, which need accurate and early detection techniques. Currently, most diagnosis and detection methods rely on the decision of neuro-specialists and radiologists to evaluate brain images, which may be time-consuming and cause human errors. This paper proposes a robust U-Net deep learning Convolutional Neural Network (CNN) model that can classify if the subject has a tumor or not based on Brain Magnetic resonance imaging (MRI) with acceptable accuracy for medical-grade application. The study built and trained the 3D U-Net CNN including encoding/decoding relationship architecture to perform the brain tumor segmentation because it requires fewer training images and provides more precise segmentation. The algorithm consists of three parts; the first part, the downsampling part, the bottleneck part, and the optimum part. The resultant semantic maps are inserted into the decoder fraction to obtain the full-resolution probability maps. The developed U-Net architecture has been applied on the MRI scan brain tumor segmentation dataset in MICCAI BraTS 2017. The results using Matlab-based toolbox indicate that the proposed architecture has been successfully evaluated and experienced for MRI datasets of brain tumor segmentation including 336 images as training data and 125 images for validation. This work demonstrated comparative performance and successful feasibility of implementing U-Net CNN architecture in an automated framework of brain tumor segmentations in Fluid-attenuated inversion recovery (FLAIR) MR Slices. The developed U-Net CNN model succeeded in performing the brain tumor segmentation task to classify the input brain images into a tumor or not based on the MRI dataset.

Author Biographies

Wasan M. Jwaid, University of Thi-Qar

Assistant Teacher

Department of Banking and Finance Administration and Economics

Zainab Shaker Matar Al-Husseini, Imam Ja'afar Al-Sadiq University

Assistant Teacher

Department of Computer Technology Engineering

College of Information Technology

Ahmad H. Sabry, Universiti Tenaga Nasional

Doctor of Control and Automation Engineering

Department of Institute of Sustainable Energy

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Published

2021-08-31

How to Cite

Jwaid, W. M., Al-Husseini, Z. S. M., & Sabry, A. H. . (2021). Development of brain tumor segmentation of magnetic resonance imaging (MRI) using U-Net deep learning. Eastern-European Journal of Enterprise Technologies, 4(9(112), 23–31. https://doi.org/10.15587/1729-4061.2021.238957

Issue

Vol. 4 No. 9(112) (2021): Information and controlling system

Section

Information and controlling system

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Copyright (c) 2021 Wasan M. Jwaid, Zainab Shaker Matar Al-Husseini, Ahmad H. Sabry

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