Neural network diagnosis of malignant skin cancers using principal component analysis as a preprocessor

Benyamin Kusumo Putro, Aripin Ariyanto

Research output: Contribution to conferencePaperpeer-review

14 Citations (Scopus)

Abstract

This paper presents an artificial neural network which is used to separate the malignant melanoma from benign categories of skin cancers based on cancer shapes and their relative color. To reduce the computational complexities, while increasing the possibility of not being trapped in local minima of the Back-propagation neural network, we applied PCA (principal component analysis) to the originally training patterns, and utilized a cross entropy error function between the output and the target patterns. By using this method, more built-in features the cancer image through its color and the cancer shapes could be used as the input of the system, leading to higher accuracy of finding the differences between malignant cancer from the benign one. Using this approach, for reasonably balance of training/testing sets, above 91,8% of correct classification of malignant and benign cancers could be obtained.

Original languageEnglish
Pages310-315
Number of pages6
Publication statusPublished - 1 Jan 1998
EventProceedings of the 1998 IEEE International Joint Conference on Neural Networks. Part 1 (of 3) - Anchorage, AK, USA
Duration: 4 May 19989 May 1998

Conference

ConferenceProceedings of the 1998 IEEE International Joint Conference on Neural Networks. Part 1 (of 3)
CityAnchorage, AK, USA
Period4/05/989/05/98

Fingerprint Dive into the research topics of 'Neural network diagnosis of malignant skin cancers using principal component analysis as a preprocessor'. Together they form a unique fingerprint.

Cite this