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Neural vs. statistical classifier in conjunction with genetic algorithm feature selection in digital mammography

conference contribution
posted on 06.12.2017, 00:00 by P Zhang, Brijesh VermaBrijesh Verma, K Kumar
Digital mammography is one of the most suitable methods for early detection of breast cancer. It uses digital mammograms to find suspicious areas containing benign and malignant microcalcifications. However, it is very difficult to distinguish benign and malignant microcalcifications. This is reflected in the high percentage of unnecessary biopsies that are performed and many deaths caused by late detection or misdiagnosis. A computer based feature selection and classification system can provide a second opinion to the radiologists in assessment of microcalcifications. The research proposes and investigates a neural-genetic algorithm for feature selection in conjunction with neural and statistical classifiers to classify microcalcification patterns in digital mammograms. The obtained results show that the proposed approach is able to find an appropriate feature subset and neural classifier achieves better results than two statistical models.

History

Start Page

1206

End Page

1213

Number of Pages

8

Start Date

08/12/2003

Finish Date

12/12/2003

ISBN-10

0780378040

Location

Canberra, Australia

Publisher

IEEE - Press

Place of Publication

Piscataway, NJ

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

Griffith University; School of Information Technology;

Era Eligible

Yes

Name of Conference

Congress on Evolutionary Computation