An improved Internet-based melanoma screening system with dermatologist-like tumor area extraction algorithm

Hitoshi Iyatomi; Hiroshi Oka; M. Emre Celebi; Masahiro Hashimoto; Masafumi Hagiwara; Masaru Tanaka; Koichi Ogawa

doi:10.1016/j.compmedimag.2008.06.005

An improved Internet-based melanoma screening system with dermatologist-like tumor area extraction algorithm

Hitoshi Iyatomi, Hiroshi Oka, M. Emre Celebi, Masahiro Hashimoto, Masafumi Hagiwara, Masaru Tanaka, Koichi Ogawa

情報工学科

研究成果: Article › 査読

201 被引用数 (Scopus)

抄録

In this paper, we present an Internet-based melanoma screening system. Our web server is accessible from all over the world and performs the following procedures when a remote user uploads a dermoscopy image: separates the tumor area from the surrounding skin using highly accurate dermatologist-like tumor area extraction algorithm, calculates a total of 428 features for the characterization of the tumor, classifies the tumor as melanoma or nevus using a neural network classifier, and presents the diagnosis. Our system achieves a sensitivity of 85.9% and a specificity of 86.0% on a set of 1258 dermoscopy images using cross-validation.

本文言語	English
ページ（範囲）	566-579
ページ数	14
ジャーナル	Computerized Medical Imaging and Graphics
巻	32
号	7
DOI	https://doi.org/10.1016/j.compmedimag.2008.06.005
出版ステータス	Published - 2008 10月

ASJC Scopus subject areas

放射線技術および超音波技術
放射線学、核医学およびイメージング
コンピュータビジョンおよびパターン認識
健康情報学
コンピュータグラフィックスおよびコンピュータ支援設計

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10.1016/j.compmedimag.2008.06.005

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引用スタイル

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title = "An improved Internet-based melanoma screening system with dermatologist-like tumor area extraction algorithm",

abstract = "In this paper, we present an Internet-based melanoma screening system. Our web server is accessible from all over the world and performs the following procedures when a remote user uploads a dermoscopy image: separates the tumor area from the surrounding skin using highly accurate dermatologist-like tumor area extraction algorithm, calculates a total of 428 features for the characterization of the tumor, classifies the tumor as melanoma or nevus using a neural network classifier, and presents the diagnosis. Our system achieves a sensitivity of 85.9% and a specificity of 86.0% on a set of 1258 dermoscopy images using cross-validation.",

keywords = "Computer-aided diagnosis (CAD), Dermoscopy, Melanoma, Neural network, Tumor extraction",

author = "Hitoshi Iyatomi and Hiroshi Oka and Celebi, {M. Emre} and Masahiro Hashimoto and Masafumi Hagiwara and Masaru Tanaka and Koichi Ogawa",

note = "Funding Information: This research was partially supported by the Ministry of Education, Science, Sports and Culture Grant-in-Aid for Young Scientists program (B), 17790788, 2005-2006. ",

year = "2008",

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doi = "10.1016/j.compmedimag.2008.06.005",

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T1 - An improved Internet-based melanoma screening system with dermatologist-like tumor area extraction algorithm

AU - Iyatomi, Hitoshi

AU - Oka, Hiroshi

AU - Celebi, M. Emre

AU - Hashimoto, Masahiro

AU - Hagiwara, Masafumi

AU - Tanaka, Masaru

AU - Ogawa, Koichi

N1 - Funding Information: This research was partially supported by the Ministry of Education, Science, Sports and Culture Grant-in-Aid for Young Scientists program (B), 17790788, 2005-2006.

PY - 2008/10

Y1 - 2008/10

N2 - In this paper, we present an Internet-based melanoma screening system. Our web server is accessible from all over the world and performs the following procedures when a remote user uploads a dermoscopy image: separates the tumor area from the surrounding skin using highly accurate dermatologist-like tumor area extraction algorithm, calculates a total of 428 features for the characterization of the tumor, classifies the tumor as melanoma or nevus using a neural network classifier, and presents the diagnosis. Our system achieves a sensitivity of 85.9% and a specificity of 86.0% on a set of 1258 dermoscopy images using cross-validation.

AB - In this paper, we present an Internet-based melanoma screening system. Our web server is accessible from all over the world and performs the following procedures when a remote user uploads a dermoscopy image: separates the tumor area from the surrounding skin using highly accurate dermatologist-like tumor area extraction algorithm, calculates a total of 428 features for the characterization of the tumor, classifies the tumor as melanoma or nevus using a neural network classifier, and presents the diagnosis. Our system achieves a sensitivity of 85.9% and a specificity of 86.0% on a set of 1258 dermoscopy images using cross-validation.

KW - Computer-aided diagnosis (CAD)

KW - Dermoscopy

KW - Melanoma

KW - Neural network

KW - Tumor extraction

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An improved Internet-based melanoma screening system with dermatologist-like tumor area extraction algorithm

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ASJC Scopus subject areas

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