Feature selection and classifiers for the computerized detection of mass lesions in digital mammography

Matthew A. Kupinski, Maryellen L. Giger

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

18 Scopus citations

Abstract

We have investigated various methods of feature selection for two different data classifiers used in the computerized detection of mass lesions in digital mammograms. Numerous features were extracted from abnormal and normal breast regions from a database consisting of 210 individual mammograms. A step-wise method, a genetic algorithm and individual feature analysis were employed to select a subset of features to be used with linear discriminants. Similar techniques were also employed for an artificial neural network classifier. In both tests the genetic algorithm was able to either outperform or equal the performance of other methods.

Original languageEnglish (US)
Title of host publication1997 IEEE International Conference on Neural Networks, ICNN 1997
Pages2460-2463
Number of pages4
DOIs
StatePublished - Dec 1 1997
Externally publishedYes
Event1997 IEEE International Conference on Neural Networks, ICNN 1997 - Houston, TX, United States
Duration: Jun 9 1997Jun 12 1997

Publication series

NameIEEE International Conference on Neural Networks - Conference Proceedings
Volume4
ISSN (Print)1098-7576

Conference

Conference1997 IEEE International Conference on Neural Networks, ICNN 1997
CountryUnited States
CityHouston, TX
Period6/9/976/12/97

ASJC Scopus subject areas

  • Software

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    Kupinski, M. A., & Giger, M. L. (1997). Feature selection and classifiers for the computerized detection of mass lesions in digital mammography. In 1997 IEEE International Conference on Neural Networks, ICNN 1997 (pp. 2460-2463). [614543] (IEEE International Conference on Neural Networks - Conference Proceedings; Vol. 4). https://doi.org/10.1109/ICNN.1997.614543