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Proceedings Paper

Neural network feature selection for breast cancer diagnosis
Author(s): Catherine M. Kocur; Steven K. Rogers; Kenneth W. Bauer; Jean M. Steppe; Jeffrey W. Hoffmeister
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Paper Abstract

More than 50 million women over the age of 40 are currently at risk for breast cancer in the United States. Computer-aided diagnosis, as a second opinion to radiologists, will aid in decreasing the number of false readings of mammograms. Neural network benefits are exploited at both the classification and feature selection stages in the development of a computer-aided breast cancer diagnostic system. The multilayer perceptron is used to classify and contrast three features (angular second moment, eigenmasses, and wavelets) developed to distinguish benign from malignant lesion in a database of 94 difficult-to-diagnose digitized microcalcification cases. System performance of 74 percent correct classifications is achieved. Feature selection techniques are presented which further improve performance. Neural and decision boundary-based methods are implemented, compared, and validated to isolate and remove useless features. The contribution from this analysis is an increase to 88 percent correct classification in system performance. These feature selection techniques can also process risk factor data.

Paper Details

Date Published: 6 April 1995
PDF: 14 pages
Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); doi: 10.1117/12.205202
Show Author Affiliations
Catherine M. Kocur, Air Force Institute of Technology (United States)
Steven K. Rogers, Air Force Institute of Technology (United States)
Kenneth W. Bauer, Air Force Institute of Technology (United States)
Jean M. Steppe, Air Force Institute of Technology (United States)
Jeffrey W. Hoffmeister, Air Force Armstrong Lab. (United States)


Published in SPIE Proceedings Vol. 2492:
Applications and Science of Artificial Neural Networks
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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