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.