title: SVM and neural networks comparison in mammographic CAD.
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).
authors.affiliation: CAPI Research Group, Universidad de Extremadura, 06071 - Badajoz, (SPAIN). carlos@capi.unex.es
authors.forename: Carlos J
authors.initials: CJ
authors.lastname: García-Orellana
authors.forename: Ramón
authors.initials: R
authors.lastname: Gallardo-Caballero
authors.forename: Miguel
authors.initials: M
authors.lastname: Macías-Macias
authors.forename: Horacio
authors.initials: H
authors.lastname: González-Velasco
mesh_terms: D000465:Algorithms; D001941:Breast Diseases / Q000000981:diagnostic imaging*; D002114:Calcinosis / Q000000981:diagnostic imaging*; D005103:Expert Systems; D005260:Female; D006801:Humans; D008327:Mammography / Q000379:methods*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity