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

Translated title of the contribution: Cerebral vessel classification with convolutional neural networks

Yusuf Huseyin Sahin^*, Gozde Unal

^*Corresponding author for this work

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

2 Citations (Scopus)

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.

Translated title of the contribution	Cerebral vessel classification with 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.2017.7960697
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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10.1109/SIU.2017.7960697

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Sahin, Y. H., & Unal, G. (2017). Evrişimsel Sinir Aǧlari ile Beyin Damarlarinin Siniflandirilmasi. In 2017 25th Signal Processing and Communications Applications Conference, SIU 2017 Article 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, Turkey, 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).

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

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 - http://www.scopus.com/inward/record.url?scp=85026296212&partnerID=8YFLogxK

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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