Breast cancer detection using mammography

Authors:
Gulzar A. Khuwaja
Affiliations:
Faculty of Computer Studies, Arab Open University, Kuwait
Venue:
SIP'06 Proceedings of the 5th WSEAS international conference on Signal processing
Year:
2006

Citing 3
Cited 1

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MACMESE'10 Proceedings of the 12th WSEAS international conference on Mathematical and computational methods in science and engineering

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Abstract

The presence of microcalcification clusters in mammograms contributes evidence for the detection of early stages of cancer. In this paper, a low-cost and high-speed neural network based breast cancer detection algorithm is presented. The microcalcifications are extracted with an adaptive neural network that is trained with cancer/malignant and normal/benign breast mammograms and a best accuracy rate of 99% for the classification of cancer/normal/benign is achieved.