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139470

MRI Brain Tumor Segmentation Using Deep Learning.

Article

Last updated: 22 Jan 2023

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Subjects

-

Tags

Electronics and Communications Engineering

Abstract

This work presents a method for classification and segmentation of brain tumors based on deep learning analysis of brain contrast T1 (T1c) MR images. To achieve this goal, three different deep learning networks are investigated i.e., U-Net, VGG16-Segnet, and DeepLabv3+ models. In addition, the integration of the 3D narrow-band information of the MRI volumes is imported to the input of the Convolutional Neural Network (CNN) to describe more accurately the tumor anatomy. Experimentations are performed on the MICCAI'2018 High Grade Glioma (HGG) subset of the Brain Tumor Segmentation (BraTS) Challenge, composed of 210 brain T1c MRI volumes, each of 155 cross-sections. Among the three investigated CNNs, DeepLabv3+ network achieves the highest Dice Similarity Coefficients (DSC) of 91.2%, 92.5%, 94.6% for the segmentation of the Enhancing Tumor (ET), the Tumor Core (TC), and the Whole Tumor (WT), respectively. Comparison with the related work confirms the advantages of the proposed system.

DOI

10.21608/bfemu.2021.139470

Keywords

Tumor Segmentation, Deep learning, Brain MRI

Authors

First Name

Shimaa

Last Name

Nassar

MiddleName

E.

Affiliation

Electronics and Communications Engineering (ECE) Department, Faculty of Engineering, Mansoura University, Egypt

Email

shaimaaelsabahy@yahoo.com

City

Masnoura

Orcid

-

First Name

Mohamed

Last Name

Mohamed

MiddleName

Abd El-Azim

Affiliation

Professor of Electronics and Communications Engineering (ECE) Department., Faculty of Engineering., El-Mansoura University., Mansoura., Egypt.

Email

mazim12@yahoo.com

City

Masnoura

Orcid

0000-0003-1899-3621

First Name

Ahmed

Last Name

Elnakib

MiddleName

-

Affiliation

ECE department, Faculty of Engineering, Mansoura University, Egypt

Email

nakib@mans.edu.eg

City

Masnoura

Orcid

0000-0001-6084-3622

Volume

45

Article Issue

4

Related Issue

17795

Issue Date

2020-12-01

Receive Date

2020-09-05

Publish Date

2021-01-14

Page Start

45

Page End

54

Print ISSN

1110-0923

Online ISSN

2735-4202

Article File

BFEMU_Volume 45_Issue 4_Pages 45-54.pdf

PDF . 1.6MB

Link

https://bfemu.journals.ekb.eg/article_139470.html

Detail API

https://bfemu.journals.ekb.eg/service?article_code=139470

Order

25

Type

Research Studies

Type Code

1,205

Publication Type

Journal

Publication Title

MEJ. Mansoura Engineering Journal

Publication Link

https://bfemu.journals.ekb.eg/

MainTitle

-

Details

Type

Article

Created At

22 Jan 2023