A. Bria, C. Marrocco, M. Molinara, F. Tortorella
In this paper we present a cascade-based framework to detect clusters of microcalcifications on mammograms. The algorithm is based on a sliding window technique where a detector is structured as a “cascade” of simple boosting classifiers with increasing complexity. Such a method couples the effectiveness of the cascade approach with the RankBoost algorithm that is aimed at maximizing the area under the ROC curve and represents a good choice when dealing with unbalanced data sets.
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