title: Multiclass primal support vector machines for breast density classification.
abstract: Parenchymal patterns defining the density of breast tissue are detected by advanced correlation pattern recognition in an integrated Computer-Aided Detection (CAD) and diagnosis system. Fractal signatures of density are modelled according to four clinical categories. A Support Vector Machine (SVM) in the primal formulation solves the multiclass problem using 'One-Versus-All' (OVA) and 'All-Versus-All' (AVA) decompositions, achieving 85% and 94% accuracy, respectively. Fully automated classification of breast density via a texture model derived from fractal dimension, dispersion, and lacunarity moves current qualitative methods forward to objective quantitative measures, amenable with the overarching vision of substantiating the role of density in epidemiological risk models of breast cancer.
authors.affiliation: Department of Bioengineering, Binghamton University, Binghamton, NY 13903-6000, USA. wland@binghamton.edu
authors.forename: Walker H
authors.initials: WH
authors.lastname: Land
authors.forename: Elizabeth A
authors.initials: EA
authors.lastname: Verheggen
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001940:Breast / Q000473:pathology*; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000473:pathology*; D019295:Computational Biology; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D017709:Fractals; D017143:Fuzzy Logic; D006801:Humans; D008327:Mammography / Q000706:statistics & numerical data; D015233:Models, Statistical