NEW RESULTS IN COMPUTER AIDED DIAGNOSIS (CAD) OF BREAST CANCER USING A RECENTLY DEVELOPED SVM/GRNN ORACLE HYBRID

摘要

This research consisted of evaluating diagnostic performance results using SVM outputs previously obtained from an integrated Duke/DDSM USF data set and the GRNN oracle. The SVM kernels used in this research included Additive, Multiplicative, S2000, and Spline kernels. GRNN results are presented for the following combinations of gate variables: age, mass margin (MM), age and MM, and all 6 BIRADS™ indicators plus age. For all experiments, Differential Evolution (DE) was used to train the GRNN. A summary of the DE process is described, independent of the software application. The experiments described in this paper show that the GRNN oracle, with all of the gate variable combinations, performed better than any of the individual SVM kernels alone at or below 98% sensitivity.