title: A comparison of feature selection methods for the detection of breast cancers in mammograms: adaptive sequential floating search vs. genetic algorithm.
abstract: This paper presents a comparison of feature selection methods for a unified detection of breast cancers in mammograms. A set of features, including curvilinear features, texture features, Gabor features, and multi-resolution features, were extracted from a region of 512x512 pixels containing normal tissue or breast cancer. Adaptive floating search and genetic algorithm were used for the feature selection, and a linear discriminant analysis (LDA) was used for the classification of cancer regions from normal regions. The performance is evaluated using Az the area under ROC curve. On a dataset consisting 296 normal regions and 164 cancer regions (53 masses, 56 spiculated lesions, and 55 calcifications), adaptive floating search achieved Az = 0.96 with comparison to Az = 0.93 of CHC genetic algorithm and Az = 0.90 of simple genetic algorithm.
authors.affiliation: Fischer Imaging Corporation, Denver, CO 80241, USA.
authors.forename: Y
authors.initials: Y
authors.lastname: Sun
authors.forename: C F
authors.initials: CF
authors.lastname: Babbs
authors.forename: E J
authors.initials: EJ
authors.lastname: Delp