TY - GEN
T1 - Parallel Deep CNN structure for glioma detection and classification via brain MRI Images
AU - Abd-Ellah, Mahmoud Khaled
AU - Awad, Ali Ismail
AU - Hamed, Hesham F.A.
AU - Khalaf, Ashraf A.M.
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - 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.
AB - 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.
KW - Brain tumor detection
KW - Computer-aided diagnosis
KW - Convolutional neural networks
KW - Deep learning
KW - Glioma classification
UR - http://www.scopus.com/inward/record.url?scp=85082101535&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85082101535&partnerID=8YFLogxK
U2 - 10.1109/ICM48031.2019.9021872
DO - 10.1109/ICM48031.2019.9021872
M3 - Conference contribution
AN - SCOPUS:85082101535
T3 - Proceedings of the International Conference on Microelectronics, ICM
SP - 304
EP - 307
BT - 31st International Conference on Microelectronics, ICM 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 31st International Conference on Microelectronics, ICM 2019
Y2 - 15 December 2019 through 18 December 2019
ER -