3B Konvolusyonel Sinir Aǧlariyla Beyin Dokularinin Lezyon veya Saǧlikli Olarak Siniflandirilmasi

Cem Yusuf Aydogdu; Enes Albay; Gozde Unal

doi:10.1109/SIU.2017.7960524

3B Konvolusyonel Sinir Aǧlariyla Beyin Dokularinin Lezyon veya Saǧlikli Olarak Siniflandirilmasi

Translated title of the contribution: Classification of brain tissues as lesion or healthy by 3D convolutional neural networks

Cem Yusuf Aydogdu^*, Enes Albay, Gozde Unal

^*Corresponding author for this work

Department of Artificial Intelligence and Data Engineering

Istanbul Technical University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

In this paper, a three dimensional convolutional neural network based solution is proposed for classification of brain tissues as lesion or healthy in terms of ischemic stroke disease. Three dimensional data used in this work are obtained by magnetic resonance imaging technique. Proposed method is compared with traditional methods that are in the same category, via K-fold cross validation technique in terms of sensitivity, specificity and accuracy measures. In conclusion, it is obtained nearly 89% accuracy using our proposed method. Comparing this method with others, our proposed method is the best method.

Translated title of the contribution	Classification of brain tissues as lesion or healthy by 3D convolutional neural networks
Original language	Turkish
Title of host publication	2017 25th Signal Processing and Communications Applications Conference, SIU 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781509064946
DOIs	https://doi.org/10.1109/SIU.2017.7960524
Publication status	Published - 27 Jun 2017
Event	25th Signal Processing and Communications Applications Conference, SIU 2017 - Antalya, Turkey Duration: 15 May 2017 → 18 May 2017

Publication series

Name	2017 25th Signal Processing and Communications Applications Conference, SIU 2017

Conference

Conference	25th Signal Processing and Communications Applications Conference, SIU 2017
Country/Territory	Turkey
City	Antalya
Period	15/05/17 → 18/05/17

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

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This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/SIU.2017.7960524

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Aydogdu, C. Y., Albay, E., & Unal, G. (2017). 3B Konvolusyonel Sinir Aǧlariyla Beyin Dokularinin Lezyon veya Saǧlikli Olarak Siniflandirilmasi. In 2017 25th Signal Processing and Communications Applications Conference, SIU 2017 Article 7960524 (2017 25th Signal Processing and Communications Applications Conference, SIU 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SIU.2017.7960524

Aydogdu, Cem Yusuf ; Albay, Enes ; Unal, Gozde. / 3B Konvolusyonel Sinir Aǧlariyla Beyin Dokularinin Lezyon veya Saǧlikli Olarak Siniflandirilmasi. 2017 25th Signal Processing and Communications Applications Conference, SIU 2017. Institute of Electrical and Electronics Engineers Inc., 2017. (2017 25th Signal Processing and Communications Applications Conference, SIU 2017).

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title = "3B Konvolusyonel Sinir Aǧlariyla Beyin Dokularinin Lezyon veya Saǧlikli Olarak Siniflandirilmasi",

abstract = "In this paper, a three dimensional convolutional neural network based solution is proposed for classification of brain tissues as lesion or healthy in terms of ischemic stroke disease. Three dimensional data used in this work are obtained by magnetic resonance imaging technique. Proposed method is compared with traditional methods that are in the same category, via K-fold cross validation technique in terms of sensitivity, specificity and accuracy measures. In conclusion, it is obtained nearly 89% accuracy using our proposed method. Comparing this method with others, our proposed method is the best method.",

keywords = "3D, classification, convolution, cross validation, ischemic stroke, magnetic resonance, neural networks",

author = "Aydogdu, {Cem Yusuf} and Enes Albay and Gozde Unal",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 25th Signal Processing and Communications Applications Conference, SIU 2017 ; Conference date: 15-05-2017 Through 18-05-2017",

year = "2017",

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doi = "10.1109/SIU.2017.7960524",

language = "T{\"u}rk{\c c}e",

series = "2017 25th Signal Processing and Communications Applications Conference, SIU 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2017 25th Signal Processing and Communications Applications Conference, SIU 2017",

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Aydogdu, CY, Albay, E & Unal, G 2017, 3B Konvolusyonel Sinir Aǧlariyla Beyin Dokularinin Lezyon veya Saǧlikli Olarak Siniflandirilmasi. in 2017 25th Signal Processing and Communications Applications Conference, SIU 2017., 7960524, 2017 25th Signal Processing and Communications Applications Conference, SIU 2017, Institute of Electrical and Electronics Engineers Inc., 25th Signal Processing and Communications Applications Conference, SIU 2017, Antalya, Turkey, 15/05/17. https://doi.org/10.1109/SIU.2017.7960524

3B Konvolusyonel Sinir Aǧlariyla Beyin Dokularinin Lezyon veya Saǧlikli Olarak Siniflandirilmasi. / Aydogdu, Cem Yusuf; Albay, Enes; Unal, Gozde.
2017 25th Signal Processing and Communications Applications Conference, SIU 2017. Institute of Electrical and Electronics Engineers Inc., 2017. 7960524 (2017 25th Signal Processing and Communications Applications Conference, SIU 2017).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - 3B Konvolusyonel Sinir Aǧlariyla Beyin Dokularinin Lezyon veya Saǧlikli Olarak Siniflandirilmasi

AU - Aydogdu, Cem Yusuf

AU - Albay, Enes

AU - Unal, Gozde

PY - 2017/6/27

Y1 - 2017/6/27

N2 - In this paper, a three dimensional convolutional neural network based solution is proposed for classification of brain tissues as lesion or healthy in terms of ischemic stroke disease. Three dimensional data used in this work are obtained by magnetic resonance imaging technique. Proposed method is compared with traditional methods that are in the same category, via K-fold cross validation technique in terms of sensitivity, specificity and accuracy measures. In conclusion, it is obtained nearly 89% accuracy using our proposed method. Comparing this method with others, our proposed method is the best method.

AB - In this paper, a three dimensional convolutional neural network based solution is proposed for classification of brain tissues as lesion or healthy in terms of ischemic stroke disease. Three dimensional data used in this work are obtained by magnetic resonance imaging technique. Proposed method is compared with traditional methods that are in the same category, via K-fold cross validation technique in terms of sensitivity, specificity and accuracy measures. In conclusion, it is obtained nearly 89% accuracy using our proposed method. Comparing this method with others, our proposed method is the best method.

KW - 3D

KW - classification

KW - convolution

KW - cross validation

KW - ischemic stroke

KW - magnetic resonance

KW - neural networks

UR - http://www.scopus.com/inward/record.url?scp=85026299322&partnerID=8YFLogxK

U2 - 10.1109/SIU.2017.7960524

DO - 10.1109/SIU.2017.7960524

M3 - Konferans katkısı

AN - SCOPUS:85026299322

T3 - 2017 25th Signal Processing and Communications Applications Conference, SIU 2017

BT - 2017 25th Signal Processing and Communications Applications Conference, SIU 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 25th Signal Processing and Communications Applications Conference, SIU 2017

Y2 - 15 May 2017 through 18 May 2017

ER -

Aydogdu CY, Albay E, Unal G. 3B Konvolusyonel Sinir Aǧlariyla Beyin Dokularinin Lezyon veya Saǧlikli Olarak Siniflandirilmasi. In 2017 25th Signal Processing and Communications Applications Conference, SIU 2017. Institute of Electrical and Electronics Engineers Inc. 2017. 7960524. (2017 25th Signal Processing and Communications Applications Conference, SIU 2017). doi: 10.1109/SIU.2017.7960524

3B Konvolusyonel Sinir Aǧlariyla Beyin Dokularinin Lezyon veya Saǧlikli Olarak Siniflandirilmasi

Abstract

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