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Proceedings Paper

Evolutionary programming technique for reducing complexity of artifical neural networks for breast cancer diagnosis
Author(s): Joseph Y. Lo; Walker H. Land; Clayton T. Morrison
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Paper 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.

Paper Details

Date Published: 6 June 2000
PDF: 6 pages
Proc. SPIE 3979, Medical Imaging 2000: Image Processing, (6 June 2000); doi: 10.1117/12.387635
Show Author Affiliations
Joseph Y. Lo, Duke Univ. Medical Ctr. and Duke Univ. (United States)
Walker H. Land, Binghamton Univ. (United States)
Clayton T. Morrison, Binghamton Univ. (United States)


Published in SPIE Proceedings Vol. 3979:
Medical Imaging 2000: Image Processing
Kenneth M. Hanson, Editor(s)

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