A comparative study for glioma classification using deep convolutional neural networks-Reference-Cited by-同舟云学术

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A comparative study for glioma classification using deep convolutional neural networks

Published:2021 Issue:2 Volume:18 Page:1550-1572
ISSN:1551-0018
Container-title:Mathematical Biosciences and Engineering
language:
Short-container-title:MBE

Author:

Özcan Hakan, ,Emiroğlu Bülent Gürsel,Sabuncuoğlu Hakan,Özdoğan Selçuk,Soyer Ahmet,Saygı Tahsin, , , ,

Publisher

American Institute of Mathematical Sciences (AIMS)

Subject

Applied Mathematics,Computational Mathematics,General Agricultural and Biological Sciences,Modelling and Simulation,General Medicine

Reference54 articles.

1. R. Chen, M. Smith-Cohn, A. L. Cohen, H. Colman, Glioma subclassifications and their clinical significance, Neurotherapeutics, 14 (2017), 284-297.

2. Y.-C. Liu, Y. Wang, Role of yes-associated protein 1 in gliomas: Pathologic and therapeutic aspects, Tumor Biol., 36 (2015), 2223-2227.

3. D. Persaud-Sharma, J. Burns, J. Trangle, S. Moulik, Disparities in brain cancer in the united states: A literature review of gliomas, Med. Sci. Basel Switz., 5 (2017), 16.

4. D. N. Louis, A. Perry, G. Reifenberger, A. von Deimling, D. Figarella-Branger, W. K. Cavenee, et al., The 2016 World Health Organization classification of tumors of the central nervous system: A summary, Acta Neuropathol., 131 (2016), 803-820.

5. C. Walker, A. Baborie, D. Crooks, S. Wilkins, M. D. Jenkinson, Biology, genetics and imaging of glial cell tumours, Br. J. Radiol., 84 (2011), S90-S106.

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