Can ICA Help Classify Skin Cancer and Benign Lesions?

Authors:
Christian Mies;Christoph Bauer;Günther Ackermann;Wolfgang Bäumler;Christoph Abels;Carlos García Puntonet;Manuel Rodríguez Alvarez;Elmar Wolfgang Lang
Affiliations:
-;-;-;-;-;-;-;-
Venue:
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Bio-inspired Applications of Connectionism-Part II
Year:
2001

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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.