Neural network feature selection for breast cancer diagnosis

Catherine M. Kocur; Steven K. Rogers; Kenneth W. Bauer Jr.; Jean M. Steppe; Jeffrey W. Hoffmeister M.D.

doi:10.1117/12.205202

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6 April 1995 Neural network feature selection for breast cancer diagnosis

Catherine M. Kocur, Steven K. Rogers, Kenneth W. Bauer Jr., Jean M. Steppe, Jeffrey W. Hoffmeister M.D.

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Proceedings Volume 2492, Applications and Science of Artificial Neural Networks; (1995) https://doi.org/10.1117/12.205202
Event: SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, 1995, Orlando, FL, United States

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.

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Catherine M. Kocur, Steven K. Rogers, Kenneth W. Bauer Jr., Jean M. Steppe, and Jeffrey W. Hoffmeister M.D. "Neural network feature selection for breast cancer diagnosis", Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); https://doi.org/10.1117/12.205202

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