Cilt Lezyonlarinin Şekil ve Renk Öznitelikleri Kullanilarak Iyileştirilmiş Siniflandirmasi

Enes Albay; Mustafa E. Kamaşak

doi:10.1109/SIU.2016.7495844

Cilt Lezyonlarinin Şekil ve Renk Öznitelikleri Kullanilarak Iyileştirilmiş Siniflandirmasi

Translated title of the contribution: Improved classification of skin lesions using shape and color features

Enes Albay
, Mustafa E. Kamaşak

Department of Computer Engineering

Istanbul Technical University

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

2 Citations (Scopus)

Abstract

It is very important to scan the skin lesions in general population, and to forward suspicious cases to the dermatologists. Typically, the number of dermatologists is not enough for such a wide scanning campaign. Hence, an automated system and algorithms are required that can detect suspicious skin lesions. These systems should be highly accurate and sensitive. In this work, an automated system is developed to detect suspicious skin lesions from digital images using shape and color features. This system is tested on images in the PH2 database. The developed system has 91.5% accuracy, 92.5% sensitivity and 87.5% specificity.

Translated title of the contribution	Improved classification of skin lesions using shape and color features
Original language	Turkish
Title of host publication	2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	733-736
Number of pages	4
ISBN (Electronic)	9781509016792
DOIs	https://doi.org/10.1109/SIU.2016.7495844
Publication status	Published - 20 Jun 2016
Event	24th Signal Processing and Communication Application Conference, SIU 2016 - Zonguldak, Turkey Duration: 16 May 2016 → 19 May 2016

Publication series

Name	2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings

Conference

Conference	24th Signal Processing and Communication Application Conference, SIU 2016
Country/Territory	Turkey
City	Zonguldak
Period	16/05/16 → 19/05/16

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

UN SDGs

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

SDG 3 Good Health and Well-being

Access to Document

10.1109/SIU.2016.7495844

Cite this

Albay, E., & Kamaşak, M. E. (2016). Cilt Lezyonlarinin Şekil ve Renk Öznitelikleri Kullanilarak Iyileştirilmiş Siniflandirmasi. In 2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings (pp. 733-736). Article 7495844 (2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SIU.2016.7495844

Albay, Enes ; Kamaşak, Mustafa E. / Cilt Lezyonlarinin Şekil ve Renk Öznitelikleri Kullanilarak Iyileştirilmiş Siniflandirmasi. 2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 733-736 (2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings).

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title = "Cilt Lezyonlarinin {\c S}ekil ve Renk {\"O}znitelikleri Kullanilarak Iyile{\c s}tirilmi{\c s} Siniflandirmasi",

abstract = "It is very important to scan the skin lesions in general population, and to forward suspicious cases to the dermatologists. Typically, the number of dermatologists is not enough for such a wide scanning campaign. Hence, an automated system and algorithms are required that can detect suspicious skin lesions. These systems should be highly accurate and sensitive. In this work, an automated system is developed to detect suspicious skin lesions from digital images using shape and color features. This system is tested on images in the PH2 database. The developed system has 91.5\% accuracy, 92.5\% sensitivity and 87.5\% specificity.",

keywords = "artificial neural networks, classification, computer-aided diagnosis, dermoscopy, melanoma",

author = "Enes Albay and Kama{\c s}ak, \{Mustafa E.\}",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 24th Signal Processing and Communication Application Conference, SIU 2016 ; Conference date: 16-05-2016 Through 19-05-2016",

year = "2016",

month = jun,

day = "20",

doi = "10.1109/SIU.2016.7495844",

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

series = "2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings",

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

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booktitle = "2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings",

address = "United States",

}

Albay, E & Kamaşak, ME 2016, Cilt Lezyonlarinin Şekil ve Renk Öznitelikleri Kullanilarak Iyileştirilmiş Siniflandirmasi. in 2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings., 7495844, 2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 733-736, 24th Signal Processing and Communication Application Conference, SIU 2016, Zonguldak, Turkey, 16/05/16. https://doi.org/10.1109/SIU.2016.7495844

Cilt Lezyonlarinin Şekil ve Renk Öznitelikleri Kullanilarak Iyileştirilmiş Siniflandirmasi. / Albay, Enes; Kamaşak, Mustafa E.
2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2016. p. 733-736 7495844 (2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings).

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

TY - GEN

T1 - Cilt Lezyonlarinin Şekil ve Renk Öznitelikleri Kullanilarak Iyileştirilmiş Siniflandirmasi

AU - Albay, Enes

AU - Kamaşak, Mustafa E.

PY - 2016/6/20

Y1 - 2016/6/20

N2 - It is very important to scan the skin lesions in general population, and to forward suspicious cases to the dermatologists. Typically, the number of dermatologists is not enough for such a wide scanning campaign. Hence, an automated system and algorithms are required that can detect suspicious skin lesions. These systems should be highly accurate and sensitive. In this work, an automated system is developed to detect suspicious skin lesions from digital images using shape and color features. This system is tested on images in the PH2 database. The developed system has 91.5% accuracy, 92.5% sensitivity and 87.5% specificity.

AB - It is very important to scan the skin lesions in general population, and to forward suspicious cases to the dermatologists. Typically, the number of dermatologists is not enough for such a wide scanning campaign. Hence, an automated system and algorithms are required that can detect suspicious skin lesions. These systems should be highly accurate and sensitive. In this work, an automated system is developed to detect suspicious skin lesions from digital images using shape and color features. This system is tested on images in the PH2 database. The developed system has 91.5% accuracy, 92.5% sensitivity and 87.5% specificity.

KW - artificial neural networks

KW - classification

KW - computer-aided diagnosis

KW - dermoscopy

KW - melanoma

UR - https://www.scopus.com/pages/publications/84982860433

U2 - 10.1109/SIU.2016.7495844

DO - 10.1109/SIU.2016.7495844

M3 - Konferans katkısı

AN - SCOPUS:84982860433

T3 - 2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings

SP - 733

EP - 736

BT - 2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 24th Signal Processing and Communication Application Conference, SIU 2016

Y2 - 16 May 2016 through 19 May 2016

ER -

Albay E, Kamaşak ME. Cilt Lezyonlarinin Şekil ve Renk Öznitelikleri Kullanilarak Iyileştirilmiş Siniflandirmasi. In 2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2016. p. 733-736. 7495844. (2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings). doi: 10.1109/SIU.2016.7495844

Cilt Lezyonlarinin Şekil ve Renk Öznitelikleri Kullanilarak Iyileştirilmiş Siniflandirmasi

Abstract

Publication series

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Bibliographical note

UN SDGs

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