Investigation of support vector machine for the detection of architectural distortion in mammographic imagesGuo, 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 Full text not archived in this repository. It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. To link to this item DOI: 10.1088/1742-6596/15/1/015 Abstract/SummaryThis 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.
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