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Proceedings Paper

Identification of malignant skin cancer using back-propagation learning with Kanhunen-Loeve transformation
Author(s): Benyamin Kusumoputro; Mayasari T. Palupi; Aniati Murni
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Paper Abstract

Malignant melanoma is the deadliest form of cancer, fortunately, if it is detected early, even this type of cancer may be treated successfully. In this paper, we present a novel network approach for the automated separation of melanoma from benign categories of cancer, which exhibit melanoma-like characteristics. To reduce the computational complexities, while increasing the possibility of not being trapped in local minima of the back-propagation neural network, we applied Karhunen-Loeve transformation technique to the originally training patterns. We also utilized a cross entropy error function between the output and the target patterns. Using this approach, for reasonably balance of training/testing set, about 94% of correct classification of malignant and benign cancers could be obtained.

Paper Details

Date Published: 31 March 2000
PDF: 6 pages
Proc. SPIE 4043, Optical Pattern Recognition XI, (31 March 2000); doi: 10.1117/12.381615
Show Author Affiliations
Benyamin Kusumoputro, Univ. of Indonesia (Indonesia)
Mayasari T. Palupi, Univ. of Indonesia (Indonesia)
Aniati Murni, Univ. of Indonesia (Indonesia)

Published in SPIE Proceedings Vol. 4043:
Optical Pattern Recognition XI
David P. Casasent; Tien-Hsin Chao, Editor(s)

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