Multiple SVM-RFE Using Boosting for Mammogram Classification
Sejong Yoon, Saejoon Kim · 2009
Digital mammography is an effective method to diagnose breast cancer. However, unnecessary biopsies caused by low accuracy in classifying benign abnormalities and malignant ones are challenging problem of the approach. To resolve the issue, computer aided diagnosis (CADx) using various AI techniques have been proposed. Recently, reports indicate that CADx systems can be improved by exploiting mammogram and AI algorithm-specific feature selection schemes. In this regard, we propose a modified feature selection method based on a recently developed multiple support vector machine recursive feature elimination (MSVM-RFE). Experimental results on real world digital mammograms show that our method demonstrated competitive performances.