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Evolutionary programming technique for reducing complexity of artificial neural networks for breast cancer diagnosis

Publication ,  Journal Article
Lo, JY; Land, WH; Morrison, CT
Published in: Proceedings of SPIE - The International Society for Optical Engineering
January 1, 2000

An evolutionary programming (EP) technique was investigated to reduce the complexity of artificial neural network (ANN) models that predict the outcome of mammography-induced breast biopsy. By combining input variables consisting of mammography lesion descriptors and patient history data, the ANN predicted whether the lesion was benign or malignant, which may aide in reducing the number of unnecessary benign biopsies and thus the cost of mammography screening of breast cancer. The EP has the ability to optimize the ANN both structurally and parametrically. An EP was partially optimized using a data set of 882 biopsy-proven cases from Duke University Medical Center. Although many different architectures were evolved, the best were often perceptrons with no hidden nodes. A rank ordering of the inputs was performed using twenty independent EP runs. This confirmed the predictive value of the mass margin and patient age variables, and revealed the unexpected usefulness of the history of previous breast cancer. Further work is required to improve the performance of the EP over all cases in general and calcification cases in particular.

Duke Scholars

Published In

Proceedings of SPIE - The International Society for Optical Engineering

ISSN

0277-786X

Publication Date

January 1, 2000

Volume

3979

Related Subject Headings

  • 5102 Atomic, molecular and optical physics
  • 4009 Electronics, sensors and digital hardware
  • 4006 Communications engineering
 

Citation

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Lo, J. Y., Land, W. H., & Morrison, C. T. (2000). Evolutionary programming technique for reducing complexity of artificial neural networks for breast cancer diagnosis. Proceedings of SPIE - The International Society for Optical Engineering, 3979.
Lo, J. Y., W. H. Land, and C. T. Morrison. “Evolutionary programming technique for reducing complexity of artificial neural networks for breast cancer diagnosis.” Proceedings of SPIE - The International Society for Optical Engineering 3979 (January 1, 2000).
Lo JY, Land WH, Morrison CT. Evolutionary programming technique for reducing complexity of artificial neural networks for breast cancer diagnosis. Proceedings of SPIE - The International Society for Optical Engineering. 2000 Jan 1;3979.
Lo, J. Y., et al. “Evolutionary programming technique for reducing complexity of artificial neural networks for breast cancer diagnosis.” Proceedings of SPIE - The International Society for Optical Engineering, vol. 3979, Jan. 2000.
Lo JY, Land WH, Morrison CT. Evolutionary programming technique for reducing complexity of artificial neural networks for breast cancer diagnosis. Proceedings of SPIE - The International Society for Optical Engineering. 2000 Jan 1;3979.

Published In

Proceedings of SPIE - The International Society for Optical Engineering

ISSN

0277-786X

Publication Date

January 1, 2000

Volume

3979

Related Subject Headings

  • 5102 Atomic, molecular and optical physics
  • 4009 Electronics, sensors and digital hardware
  • 4006 Communications engineering