Mass Lesions Classification in Digital Mammography using Optimal Subset of BI-RADS and Gray Level Features

Abstract

Computer-aided diagnosis of mass lesions in Digital Database for Screening Mammography (DDSM) is investigated using a recently developed SVM based on recursive feature elimination (SVM-RFE) as the classification technique. To evaluate the generalizability, computer-aided diagnosis using cross-institutional mammograms is also examined. The results in this paper indicate that using only a subset of the available set of features facilitates increased computer-aided diagnosis accuracy, and that computer-aided diagnosis accuracy using cross-institutional mammograms is generally lower than when using same-institutional mammograms.