Normal mammogram classification based on a support vector machine utilizing crossed distribution features.
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.
Chiracharit
W
W
Department of Electronics and Telecommunication Engineering, King Mongkut's University of Technology, Bangkok, Thailand.
Sun
Y
Y
Kumhom
P
P
Chamnongthai
K
K
Babbs
C
C
Delp
E
E