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Parallel Deep CNN Structure for Glioma Detection and Classification via Brain MRI Images

Abd-Ellah, Mahmoud Khaled, (författare)
Electronics and Communications Department, Al-Madina Higher Institute for Engineering and Technology, Giza, Egypt
Awad, Ali Ismail, (författare)
Luleå tekniska universitet, Digitala tjänster och system, Faculty of Engineering, Al-Alzhar University, P.O. Box 83513, Qena, Egypt
Hamed, Hesham F. A., (författare)
Department of Telecommunications Eng., Egyptian Russian University, Cairo, Egypt. Department of Communications and Electronics, Faculty of Engineering, Minia University, Minia, Egypt
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Khalaf, Ashraf A. M., (författare)
Department of Communications and Electronics, Faculty of Engineering, Minia University, Minia, Egypt
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Serie: International Conference on Microelectronics, ICM
Ingår i: IEEE-ICM 2019 CAIRO-EGYPT : The 31st International Conference on Microelectronics. - IEEE. ; s. 304-307
  • Konferensbidrag (övrigt vetenskapligt)
Abstract Ämnesord
  • <p>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.</p>


SAMHÄLLSVETENSKAP  -- Medie- och kommunikationsvetenskap -- Systemvetenskap, informationssystem och informatik med samhällsvetenskaplig inriktning (hsv//swe)
SOCIAL SCIENCES  -- Media and Communications -- Information Systems, Social aspects (hsv//eng)


Computer-aided diagnosis
brain tumor detection
deep learning
convolutional neural networks
glioma classification
Information systems

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