Can ICA Help Classify Skin Cancer and Benign Lesions?
Identifieur interne : 000300 ( Main/Exploration ); précédent : 000299; suivant : 000301Can ICA Help Classify Skin Cancer and Benign Lesions?
Auteurs : Ch. Mies [Allemagne] ; Ch. Bauer [Allemagne] ; G. Ackermann [Allemagne] ; W. B Umler [Allemagne] ; C. Abels [Allemagne] ; C. G. Puntonet [Espagne] ; M. R. Alvarez [Espagne] ; E. W. Lang [Allemagne]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2001.
Abstract
Abstract: Various neural network models for the identification and classification of different skin lesions from ALA-induced fluorescence images are presented. After different image preprocessing steps, eigenimages and independent base images are extracted using PCA and ICA, respectively. In order to extract local information in the images rather than global features, Generative Topographic Mapping is added to cluster patches of the images first and then extract local features by ICA (local ICA). These components are used to distinguish skin cancer from benign lesions. An average classification rate of 70% is obtained, which considerably exceeds the rate achieved by an experienced physician.
Url:
DOI: 10.1007/3-540-45723-2_39
Affiliations:
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Alvarez</name><affiliation wicri:level="4"><country xml:lang="fr">Espagne</country><wicri:regionArea>Departamento de Arquitectura y Tecnologia de Computadores E.T.S. Ingenieria Informatica, Universidad de Granada, 18071, Granada, E</wicri:regionArea><orgName type="university">Université de Grenade</orgName><placeName><settlement type="city">Grenade (Espagne)</settlement><region nuts="2" type="region">Andalousie</region></placeName></affiliation></author><author><name sortKey="Lang, E W" sort="Lang, E W" uniqKey="Lang E" first="E. W." last="Lang">E. W. 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