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Proceedings Paper

Computer aided detection of breast masses in mammography using support vector machine classification
Author(s): Jan Lesniak; Rianne Hupse; Michiel Kallenberg; Maurice Samulski; Rémi Blanc; Nico Karssemeijer; Gàbor Székely
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Paper Abstract

The reduction of false positive marks in breast mass CAD is an active area of research. Typically, the problem can be approached either by developing more discriminative features or by employing different classifier designs. Usually one intends to find an optimal combination of classifier configuration and small number of features to ensure high classification performance and a robust model with good generalization capabilities. In this paper, we investigate the potential benefit of relying on a support vector machine (SVM) classifier for the detection of masses. The evaluation is based on a 10-fold cross validation over a large database of screen film mammograms (10397 images). The purpose of this study is twofold: first, we assess the SVM performance compared to neural networks (NNet), k-nearest neighbor classification (k-NN) and linear discriminant analysis (LDA). Second, we study the classifiers' performances when using a set of 30 and a set of 73 region-based features. The CAD performance is quantified by the mean sensitivity in 0.05 to 1 false positives per exam on the free-response receiver operating characteristic curve. The best mean exam sensitivities found were 0.545, 0.636, 0.648, 0.675 for LDA, k-NN, NNet and SVM. K-NN and NNet proved to be stable against variation of the featuresets. Conversely, LDA and SVM exhibited an increase in performance when adding more features. It is concluded that with an SVM a more pronounced reduction of false positives is possible, given that a large number of cases and features are available.

Paper Details

Date Published: 8 March 2011
PDF: 7 pages
Proc. SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis, 79631K (8 March 2011); doi: 10.1117/12.878140
Show Author Affiliations
Jan Lesniak, ETH Zurich (Switzerland)
Rianne Hupse, Radboud Univ. Nijmegen Medical Ctr. (Netherlands)
Michiel Kallenberg, Radboud Univ. Nijmegen Medical Ctr. (Netherlands)
Maurice Samulski, Radboud Univ. Nijmegen Medical Ctr. (Netherlands)
Rémi Blanc, ETH Zurich (Switzerland)
Nico Karssemeijer, Radboud Univ. Nijmegen Medical Ctr. (Netherlands)
Gàbor Székely, ETH Zurich (Switzerland)


Published in SPIE Proceedings Vol. 7963:
Medical Imaging 2011: Computer-Aided Diagnosis
Ronald M. Summers; Bram van Ginneken, Editor(s)

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