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 neural 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.
|Number of pages||6|
|Journal||Proceedings of SPIE - The International Society for Optical Engineering|
|Publication status||Published - 1 Jan 2000|
|Event||Optical Pattern Recognition XI - Orlando, FL, USA|
Duration: 26 Apr 2000 → 27 Apr 2000