MRI Brain Tumor Segmentation Using Deep Learning.
Nassar, S., Mohamed, M., Elnakib, A. (2021). MRI Brain Tumor Segmentation Using Deep Learning.. EKB Journal Management System, 45(4), 45-54. doi: 10.21608/bfemu.2021.139470
Shimaa E. Nassar; Mohamed Abd El-Azim Mohamed; Ahmed Elnakib. "MRI Brain Tumor Segmentation Using Deep Learning.". EKB Journal Management System, 45, 4, 2021, 45-54. doi: 10.21608/bfemu.2021.139470
Nassar, S., Mohamed, M., Elnakib, A. (2021). 'MRI Brain Tumor Segmentation Using Deep Learning.', EKB Journal Management System, 45(4), pp. 45-54. doi: 10.21608/bfemu.2021.139470
Nassar, S., Mohamed, M., Elnakib, A. MRI Brain Tumor Segmentation Using Deep Learning.. EKB Journal Management System, 2021; 45(4): 45-54. doi: 10.21608/bfemu.2021.139470

MRI Brain Tumor Segmentation Using Deep Learning.

MEJ- Mansoura Engineering Journal
Article 25, Volume 45, Issue 4, December 2020, Page 45-54  XML PDF (1.51 MB)
Document Type: Research Studies
DOI: 10.21608/bfemu.2021.139470
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Authors
Shimaa E. Nassar1; Mohamed Abd El-Azim Mohamedorcid 2; Ahmed Elnakib email orcid 3
1Electronics and Communications Engineering (ECE) Department, Faculty of Engineering, Mansoura University, Egypt
2Professor of Electronics and Communications Engineering (ECE) Department., Faculty of Engineering., El-Mansoura University., Mansoura., Egypt.
3ECE department, Faculty of Engineering, Mansoura University, Egypt
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.
Keywords
Tumor Segmentation; Deep Learning; Brain MRI
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