Evrişimsel Sinir Aǧlari ile Beyin Damarlarinin Siniflandirilmasi

Yusuf Huseyin Sahin; Gozde Unal

doi:10.1109/SIU.2017.7960697

Evrişimsel Sinir Aǧlari ile Beyin Damarlarinin Siniflandirilmasi

Yusuf Huseyin Sahin^*
, Gozde Unal

^*Bu çalışma için yazışmadan sorumlu yazar

Araştırma çıktısı: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › Hakem

2 Atıf (Scopus)

Özet

Analysing brain magnetic resonance angiography (MRA) images is important for detecting arteriovenous malformations and aneurysms. To detect these diseases, extracting the vessel structure in the image can be seen as a first step. In this paper, it was aimed to classify the cubic image parts obtained from brain MRA images according to whether they belong to vein structure or not. For this purpose, a 9 layers deep convolutional neural network (CNN) architecture is designed. With the model trained using this architecture, 85% accuracy was obtained in the classification performed on the test data.

Tercüme edilen katkı başlığı	Cerebral vessel classification with 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.2017.7960697
Yayın durumu	Yayınlandı - 27 Haz 2017
Etkinlik	25th Signal Processing and Communications Applications Conference, SIU 2017 - Antalya, Türkiye 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	Türkiye
Şehir	Antalya
Periyot	15/05/17 → 18/05/17

Bibliyografik not

Publisher Copyright:
© 2017 IEEE.

Keywords

cerebral vessel classification
convolutional neural networks
deep learning
magnetic resonance angiography (MRA)

Belgeye Erişim

10.1109/SIU.2017.7960697

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

Sahin, Y. H., & Unal, G. (2017). Evrişimsel Sinir Aǧlari ile Beyin Damarlarinin Siniflandirilmasi. In 2017 25th Signal Processing and Communications Applications Conference, SIU 2017 Makale 7960697 (2017 25th Signal Processing and Communications Applications Conference, SIU 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SIU.2017.7960697

@inproceedings{cff0a38d772e47588e3cfa6833fec3cd,

title = "Evri{\c s}imsel Sinir Aǧlari ile Beyin Damarlarinin Siniflandirilmasi",

abstract = "Analysing brain magnetic resonance angiography (MRA) images is important for detecting arteriovenous malformations and aneurysms. To detect these diseases, extracting the vessel structure in the image can be seen as a first step. In this paper, it was aimed to classify the cubic image parts obtained from brain MRA images according to whether they belong to vein structure or not. For this purpose, a 9 layers deep convolutional neural network (CNN) architecture is designed. With the model trained using this architecture, 85\% accuracy was obtained in the classification performed on the test data.",

keywords = "cerebral vessel classification, convolutional neural networks, deep learning, magnetic resonance angiography (MRA)",

author = "Sahin, \{Yusuf Huseyin\} 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.2017.7960697",

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",

}

Sahin, YH & Unal, G 2017, Evrişimsel Sinir Aǧlari ile Beyin Damarlarinin Siniflandirilmasi. in 2017 25th Signal Processing and Communications Applications Conference, SIU 2017., 7960697, 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, Türkiye, 15/05/17. https://doi.org/10.1109/SIU.2017.7960697

Evrişimsel Sinir Aǧlari ile Beyin Damarlarinin Siniflandirilmasi. / Sahin, Yusuf Huseyin ; Unal, Gozde.
2017 25th Signal Processing and Communications Applications Conference, SIU 2017. Institute of Electrical and Electronics Engineers Inc., 2017. 7960697 (2017 25th Signal Processing and Communications Applications Conference, SIU 2017).

Araştırma çıktısı: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › Hakem

TY - GEN

T1 - Evrişimsel Sinir Aǧlari ile Beyin Damarlarinin Siniflandirilmasi

AU - Sahin, Yusuf Huseyin

AU - Unal, Gozde

PY - 2017/6/27

Y1 - 2017/6/27

N2 - Analysing brain magnetic resonance angiography (MRA) images is important for detecting arteriovenous malformations and aneurysms. To detect these diseases, extracting the vessel structure in the image can be seen as a first step. In this paper, it was aimed to classify the cubic image parts obtained from brain MRA images according to whether they belong to vein structure or not. For this purpose, a 9 layers deep convolutional neural network (CNN) architecture is designed. With the model trained using this architecture, 85% accuracy was obtained in the classification performed on the test data.

AB - Analysing brain magnetic resonance angiography (MRA) images is important for detecting arteriovenous malformations and aneurysms. To detect these diseases, extracting the vessel structure in the image can be seen as a first step. In this paper, it was aimed to classify the cubic image parts obtained from brain MRA images according to whether they belong to vein structure or not. For this purpose, a 9 layers deep convolutional neural network (CNN) architecture is designed. With the model trained using this architecture, 85% accuracy was obtained in the classification performed on the test data.

KW - cerebral vessel classification

KW - convolutional neural networks

KW - deep learning

KW - magnetic resonance angiography (MRA)

UR - https://www.scopus.com/pages/publications/85026296212

U2 - 10.1109/SIU.2017.7960697

DO - 10.1109/SIU.2017.7960697

M3 - Konferans katkısı

AN - SCOPUS:85026296212

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 -

Evrişimsel Sinir Aǧlari ile Beyin Damarlarinin Siniflandirilmasi

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Yayın serisi

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

Bibliyografik not

Keywords

Belgeye Erişim

Diğer Dosyalar ve Linkler

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