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SVM and neural networks comparison in mammographic CAD
Carlos J García-Orellana1, Ramón Gallardo-Caballero, Miguel Macías-Macias
1CAPI Research Group, Universidad de Extremadura, 06071 - Badajoz, (SPAIN). carlos@capi.unex.es
Abstract:
The purpose of this work is to compare the performance of Support Vector Machines (SVM) and Multi-Layer Perceptron (MLP) in the task of detection and diagnosis of microcalcification clusters in mammograms (MCCs). As data source, the "Digital Database for Screening Mammography" (DDSM) was used. The results show a similar performance for SVM and MLP, in both tasks, detection and diagnosis (slightly better for MLP in detection).