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

Cem Yusuf Aydogdu^*, Enes Albay, Gozde Unal

^*Bu çalışma için yazışmadan sorumlu yazar

Yapay Zeka ve Veri Mühendisliği Bölümü

Istanbul Technical University

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

1 Atıf (Scopus)

Özet

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.

Tercüme edilen katkı başlığı	Classification of brain tissues as lesion or healthy by 3D convolutional neural networks
Orijinal dil	Türkçe
Ana bilgisayar yayını başlığı	2017 25th Signal Processing and Communications Applications Conference, SIU 2017
Yayınlayan	Institute of Electrical and Electronics Engineers Inc.
ISBN (Elektronik)	9781509064946
DOI'lar	https://doi.org/10.1109/SIU.2017.7960524
Yayın durumu	Yayınlandı - 27 Haz 2017
Etkinlik	25th Signal Processing and Communications Applications Conference, SIU 2017 - Antalya, Turkey Süre: 15 May 2017 → 18 May 2017

Yayın serisi

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

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	25th Signal Processing and Communications Applications Conference, SIU 2017
Ülke/Bölge	Turkey
Şehir	Antalya
Periyot	15/05/17 → 18/05/17

Bibliyografik not

Publisher Copyright:
© 2017 IEEE.

Keywords

3D
classification
convolution
cross validation
ischemic stroke
magnetic resonance
neural networks

BM SKH

Bu sonuç, aşağıdaki Sürdürülebilir Kalkınma Hedefine/Hedeflerine katkıda bulunur

Belgeye Erişim

10.1109/SIU.2017.7960524

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

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 Makale 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).

@inproceedings{155bf89126484bf3879a317a69b8254d,

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",

month = jun,

day = "27",

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",

address = "United States",

}

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).

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

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

Özet

Yayın serisi

???event.eventtypes.event.conference???

Bibliyografik not

Keywords

BM SKH

Belgeye Erişim

Diğer Dosyalar ve Linkler

Parmak izi

Alıntı Yap