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Segmentation of Brain Tumors Using Three-Dimensional Convolutional Neural Network on MRI Images 3D MedImg-CNN

Segmentation of Brain Tumors Using Three-Dimensional Convolutional Neural Network on MRI Images 3D MedImg-CNN

Ahmed Kharrat, Mahmoud Neji
Copyright: © 2021 |Volume: 15 |Issue: 4 |Pages: 17
ISSN: 1557-3958|EISSN: 1557-3966|EISBN13: 9781799859857|DOI: 10.4018/IJCINI.20211001.oa4
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MLA

Kharrat, Ahmed, and Mahmoud Neji. "Segmentation of Brain Tumors Using Three-Dimensional Convolutional Neural Network on MRI Images 3D MedImg-CNN." IJCINI vol.15, no.4 2021: pp.1-17. http://doi.org/10.4018/IJCINI.20211001.oa4

APA

Kharrat, A. & Neji, M. (2021). Segmentation of Brain Tumors Using Three-Dimensional Convolutional Neural Network on MRI Images 3D MedImg-CNN. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 15(4), 1-17. http://doi.org/10.4018/IJCINI.20211001.oa4

Chicago

Kharrat, Ahmed, and Mahmoud Neji. "Segmentation of Brain Tumors Using Three-Dimensional Convolutional Neural Network on MRI Images 3D MedImg-CNN," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 15, no.4: 1-17. http://doi.org/10.4018/IJCINI.20211001.oa4

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Abstract

We consider the problem of fully automatic brain tumor segmentation in MR images containing glioblastomas. We propose a three Dimensional Convolutional Neural Network (3D MedImg-CNN) approach which achieves high performance while being extremely efficient, a balance that existing methods have struggled to achieve. Our 3D MedImg-CNN is formed directly on the raw image modalities and thus learn a characteristic representation directly from the data. We propose a new cascaded architecture with two pathways that each model normal details in tumors. Fully exploiting the convolutional nature of our model also allows us to segment a complete cerebral image in one minute. The performance of the proposed 3D MedImg-CNN with CNN segmentation method is computed using dice similarity coefficient (DSC). In experiments on the 2013, 2015 and 2017 BraTS challenges datasets; we unveil that our approach is among the most powerful methods in the literature, while also being very effective.