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Neural-association of microcalcification patterns for their reliable classification in digital mammography

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journal contribution
posted on 2017-12-06, 00:00 authored by Rinku Panchal, Brijesh Verma
Breast cancer continues to be the most common cause of cancer deaths in women. Early detection of breast cancer is significant for better prognosis. Digital Mammography currently offers the best control strategy for the early detection of breast cancer. The research work in this paper investigates the significance of neural-association of microcalcification patterns for their reliable classification in digital mammograms. The proposed technique explores the auto-associative abilities of a neural network approach to regenerate the composite of its learned patterns most consistent with the new information, thus the regenerated patterns can uniquely signify each input class and improve the overall classification. Two types of features: computer extracted (gray level based statistical) features and human extracted (radiologists' interpretation) features are used for the classification of calcification type of breast abnormalities. The proposed technique attained the highest 90.5% classification rate on the calcification testing dataset.

Funding

Category 1 - Australian Competitive Grants (this includes ARC, NHMRC)

History

Volume

20

Issue

7

Start Page

971

End Page

983

Number of Pages

13

ISSN

0218-0014

Location

Singapore

Publisher

World Scientific Publishing

Language

en-aus

Peer Reviewed

  • Yes

Open Access

  • No

External Author Affiliations

Faculty of Business and Informatics; TBA Research Institute;

Era Eligible

  • Yes

Journal

International journal of pattern recognition and artificial intelligence.