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Investigation of support vector machine for the detection of architectural distortion in mammographic images

Guo, Q., Shao, J. and Ruiz, V. (2005) Investigation of support vector machine for the detection of architectural distortion in mammographic images. In: Prosser, S. and Yan, Y. (eds.) Sensors & Their Applications XIII. Journal of Physics Conference Series, 15. Iop Publishing Ltd, Bristol, pp. 88-94. ISBN 1742-6588

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To link to this item DOI: 10.1088/1742-6596/15/1/015

Abstract/Summary

This paper investigates detection of architectural distortion in mammographic images using support vector machine. Hausdorff dimension is used to characterise the texture feature of mammographic images. Support vector machine, a learning machine based on statistical learning theory, is trained through supervised learning to detect architectural distortion. Compared to the Radial Basis Function neural networks, SVM produced more accurate classification results in distinguishing architectural distortion abnormality from normal breast parenchyma.

Item Type:Book or Report Section
Divisions:Faculty of Science
ID Code:14389
Uncontrolled Keywords:COMPUTER-AIDED DETECTION, SCREENING MAMMOGRAPHY, SENSITIVITY, BREAST
Publisher:Iop Publishing Ltd

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