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

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
PublisherSociety of Photo-Optical Instrumentation Engineers
Volume3979
StatePublished - 2000
Externally publishedYes
EventMedical Imaging 2000: Image Processing - San Diego, CA, USA
Duration: Feb 14 2000Feb 17 2000

Other

OtherMedical Imaging 2000: Image Processing
CitySan Diego, CA, USA
Period2/14/002/17/00

Fingerprint

programming
Evolutionary algorithms
breast
cancer
Mammography
Biopsy
Neural networks
lesions
histories
calcification
self organizing systems
margins
Screening
screening
costs
Costs

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Condensed Matter Physics

Cite this

Lo, J. Y., Land, W. H., & Morrison, C. T. (2000). Evolutionary programming technique for reducing complexity of artificial neural networks for breast cancer diagnosis. In Proceedings of SPIE - The International Society for Optical Engineering (Vol. 3979). Society of Photo-Optical Instrumentation Engineers.

Evolutionary programming technique for reducing complexity of artificial neural networks for breast cancer diagnosis. / Lo, Joseph Y.; Land, Walker H.; Morrison, Clayton T.

Proceedings of SPIE - The International Society for Optical Engineering. Vol. 3979 Society of Photo-Optical Instrumentation Engineers, 2000.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Lo, JY, Land, WH & Morrison, CT 2000, Evolutionary programming technique for reducing complexity of artificial neural networks for breast cancer diagnosis. in Proceedings of SPIE - The International Society for Optical Engineering. vol. 3979, Society of Photo-Optical Instrumentation Engineers, Medical Imaging 2000: Image Processing, San Diego, CA, USA, 2/14/00.
Lo JY, Land WH, Morrison CT. Evolutionary programming technique for reducing complexity of artificial neural networks for breast cancer diagnosis. In Proceedings of SPIE - The International Society for Optical Engineering. Vol. 3979. Society of Photo-Optical Instrumentation Engineers. 2000
Lo, Joseph Y. ; Land, Walker H. ; Morrison, Clayton T. / 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 Society of Photo-Optical Instrumentation Engineers, 2000.
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