Identification of malignant skin cancer using back-propagation learning with Kanhunen-Loeve transformation

Benyamin Kusumoputro; Mayasari T. Palupi; Aniati Murni

doi:10.1117/12.381615

31 March 2000 Identification of malignant skin cancer using back-propagation learning with Kanhunen-Loeve transformation

Benyamin Kusumoputro, Mayasari T. Palupi, Aniati Murni

Author Affiliations +

Proceedings Volume 4043, Optical Pattern Recognition XI; (2000) https://doi.org/10.1117/12.381615
Event: AeroSense 2000, 2000, Orlando, FL, United States

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.

Citation Download Citation

Benyamin Kusumoputro, Mayasari T. Palupi, and Aniati Murni "Identification of malignant skin cancer using back-propagation learning with Kanhunen-Loeve transformation", Proc. SPIE 4043, Optical Pattern Recognition XI, (31 March 2000); https://doi.org/10.1117/12.381615

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