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Significance of neural association of microcalcification patterns for their classification in digital mammography

conference contribution
posted on 2017-12-06, 00:00 authored by Rinku PanchalRinku Panchal, Brijesh Verma
Breast cancer continues to be the most common cause of cancer deaths among women. Early detection of breast cancer is vital to improve its prognosis. Digital Mammography currently offers the best control strategy for early detection of breast cancer. The research work in this paper investigates the significance of neural association of microcalcification patterns for their classification in digital mammogram. The proposed technique explores the auto-associative abilities of a neural network approach to regenerate the composite of learned patterns most consistent with new information, which uniquely signifies each class of input patterns, and improves the overall classification. It uses two types of features: computer extracted (grey level based statistical) features from mammogram; and human extracted (radiologists’ interpretation) features to classify different types of breast abnormalities. On testing dataset it attained 90.5% classification rate for calcification cases and 89.7% classification rate for mass cases.


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


Parent Title

Complex 2004 : Proceedings of the 7th Asia-Pacific Complex Systems Conference, Cairns Convention Centre, Cairns, Australia, 6-10 December 2004.

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End Page


Number of Pages


Start Date





Cairns, Australia


Central Queensland University

Place of Publication

Rockhampton, Qld.

Peer Reviewed

  • Yes

Open Access

  • No

External Author Affiliations

Faculty of Informatics and Communication; TBA Research Institute;

Era Eligible

  • Yes

Name of Conference

Asia-Pacific Conference on Complex Systems

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