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

Enes Albay, Mustafa E. Kamaşak

Bilgisayar Mühendisliği Bölümü

Istanbul Technical University

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

2 Atıf (Scopus)

Özet

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.

Tercüme edilen katkı başlığı	Improved classification of skin lesions using shape and color features
Orijinal dil	Türkçe
Ana bilgisayar yayını başlığı	2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings
Yayınlayan	Institute of Electrical and Electronics Engineers Inc.
Sayfalar	733-736
Sayfa sayısı	4
ISBN (Elektronik)	9781509016792
DOI'lar	https://doi.org/10.1109/SIU.2016.7495844
Yayın durumu	Yayınlandı - 20 Haz 2016
Etkinlik	24th Signal Processing and Communication Application Conference, SIU 2016 - Zonguldak, Turkey Süre: 16 May 2016 → 19 May 2016

Yayın serisi

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

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

???event.eventtypes.event.conference???	24th Signal Processing and Communication Application Conference, SIU 2016
Ülke/Bölge	Turkey
Şehir	Zonguldak
Periyot	16/05/16 → 19/05/16

Bibliyografik not

Publisher Copyright:
© 2016 IEEE.

Keywords

artificial neural networks
classification
computer-aided diagnosis
dermoscopy
melanoma

BM SKH

Bu sonuç, aşağıdaki Sürdürülebilir Kalkınma Hedefine/Hedeflerine katkıda bulunur

Belgeye Erişim

10.1109/SIU.2016.7495844

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

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). Makale 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).

@inproceedings{e0e4c22a85ae42a08873d09f29ebb32d,

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.",

pages = "733--736",

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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).

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

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 - http://www.scopus.com/inward/record.url?scp=84982860433&partnerID=8YFLogxK

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

Özet

Yayın serisi

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

Bibliyografik not

Keywords

BM SKH

Belgeye Erişim

Diğer Dosyalar ve Linkler

Parmak izi

Alıntı Yap