Mies, Ch. and Bauer, Ch. and Ackermann, G. and Bäumler, W. and Abels, C. and Puntonet, C. G. and Alvarez, M. R. and Lang, Elmar (2001) Can ICA Help Classify Skin Cancer and Benign Lesions? In: Mira, José, (ed.) Bio-inspired applications of connectionism. Proceedings / 6th International Work-Conference on Artificial and Natural Neural Networks, IWANN 2001, Granada, Spain, June 13 - 15, 2001. Part 2. Lecture notes in computer science, 2085. Springer, Berlin, pp. 328-335. ISBN 3-540-42237-4 (print und e-book).
Full text not available from this repository.
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.
|Item Type:||Book Section|
|Institutions:||Biology, Preclinical Medicine > Institut für Biophysik und physikalische Biochemie > Prof. Dr. Elmar Lang|
|Subjects:||500 Science > 570 Life sciences|
|Created at the University of Regensburg:||Unknown|
|Deposited On:||28 Sep 2010 06:13|
|Last Modified:||28 Sep 2010 06:13|