title: Normal mammogram classification based on a support vector machine utilizing crossed distribution features.
abstract: Automatic classification of normal mammograms, which constitute a majority of screening mammograms, is a new approach to computer-aided diagnosis of breast cancer. This approach may be limited, however, by non-separable "crossed" distributions of features that are extracted from digitized mammograms. This work presents a method of mapping such non-separable input features into a new set of separable features that can be utilized, together with ordinary "uncrossed" features, by a support vector machine (SVM) classifier. The results of the proposed scheme show improved performance with 80% sensitivity and 95% specificity.
authors.affiliation: Department of Electronics and Telecommunication Engineering, King Mongkut's University of Technology, Bangkok, Thailand.
authors.forename: W
authors.initials: W
authors.lastname: Chiracharit
authors.forename: Y
authors.initials: Y
authors.lastname: Sun
authors.forename: P
authors.initials: P
authors.lastname: Kumhom
authors.forename: K
authors.initials: K
authors.lastname: Chamnongthai
authors.forename: C
authors.initials: C
authors.lastname: Babbs
authors.forename: E
authors.initials: E
authors.lastname: Delp