Application of evolutionary programming and probabilistic neural networks to breast cancer diagnosis

Joseph Y. Lo, Walker H. Land, Clayton T. Morrison

Research output: Contribution to conferencePaper

4 Scopus citations

Abstract

Two novel artificial neural network techniques, evolutionary programming (EP) and probabilistic neural networks (PNN), were applied to the problem of breast cancer diagnosis. The EP is a stochastic optimization technique with the ability to mutate both network connections and weight values. The PNN has the ability to produce optimal Bayesian decision making given sufficient training data. Both techniques offer potential improvements over the well-studied, classic backpropagation networks. Preliminary performances of these new techniques were comparable to but slightly worse than the classic networks. In on-going work, these new techniques will be optimized further and should produce results greater than or equal to the classic networks, but with more information content and confidence.

Original languageEnglish (US)
Pages3712-3716
Number of pages5
StatePublished - Dec 1 1999
Externally publishedYes
EventInternational Joint Conference on Neural Networks (IJCNN'99) - Washington, DC, USA
Duration: Jul 10 1999Jul 16 1999

Other

OtherInternational Joint Conference on Neural Networks (IJCNN'99)
CityWashington, DC, USA
Period7/10/997/16/99

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Fingerprint Dive into the research topics of 'Application of evolutionary programming and probabilistic neural networks to breast cancer diagnosis'. Together they form a unique fingerprint.

  • Cite this

    Lo, J. Y., Land, W. H., & Morrison, C. T. (1999). Application of evolutionary programming and probabilistic neural networks to breast cancer diagnosis. 3712-3716. Paper presented at International Joint Conference on Neural Networks (IJCNN'99), Washington, DC, USA, .