Breast Cancer Detection from FNA Using SVM and RBF Classifier

In this paper, we consider the benefits of applying support vector machines (SVMs) and radial basis function (RBF) for breast cancer detection. The Wisconsin diagnosis breast cancer (WDBC) dataset is used in the classification experiments; the dataset was generated from fine needle aspiration (FNA) samples through image processing. The 1-norm C-SVM (L1-SVM) and 2-norm C-SVM (L2-SVM) are applied, for which the grid search based on gradient descent based on validation error estimate (GDVEE) are developed to improve the detection accuracy.Experimental results demonstrate that SVM classifiers with the proposed automatic parameter tuning systems and the RBF classifier can be used as one of most efficient tools for breast cancer detection, with the detection accuracy up to 98%.

Breast Cancer Detection from FNA Using SVM and RBF Classifier Keywords:

Breast cancer detection , Parameter tuning , Radial basis function networks , Support vector machines