Parallel Deep CNN structure for glioma detection and classification via brain MRI Images

Mahmoud Khaled Abd-Ellah, Ali Ismail Awad, Hesham F.A. Hamed, Ashraf A.M. Khalaf

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)

Abstract

Although most brain tumor diagnosis studies have focused on tumor segmentation and localization operations, few studies have focused on tumor detection as a time- and effort-saving process. This study introduces a new network structure for accurate glioma tumor detection and classification using two parallel deep convolutional neural networks (PDCNNs). The proposed structure is designed to identify the presence and absence of a brain tumor in MRI images and classify the type of tumor images as high-grade gliomas (HGGs, i.e., glioblastomas) or low-grade gliomas (LGGs). The introduced PDCNNs structure takes advantage of both global and local features extracted from the two parallel stages. The proposed structure is not only accurate but also efficient, as the convolutional layers are more accurate because they learn spatial features, and they are efficient in the testing phase since they reduce the number of weights, which reduces the memory usage and runtime. Simulation experiments were accomplished using an MRI dataset extracted from the BraTS 2017 database. The obtained results show that the proposed parallel network structure outperforms other detection and classification methods in the literature.

Original languageEnglish
Title of host publication31st International Conference on Microelectronics, ICM 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages304-307
Number of pages4
ISBN (Electronic)9781728140582
DOIs
Publication statusPublished - Dec 2019
Externally publishedYes
Event31st International Conference on Microelectronics, ICM 2019 - Cairo, Egypt
Duration: Dec 15 2019Dec 18 2019

Publication series

NameProceedings of the International Conference on Microelectronics, ICM
Volume2019-December

Conference

Conference31st International Conference on Microelectronics, ICM 2019
Country/TerritoryEgypt
CityCairo
Period12/15/1912/18/19

Keywords

  • Brain tumor detection
  • Computer-aided diagnosis
  • Convolutional neural networks
  • Deep learning
  • Glioma classification

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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