Specificity enhancement in classification of breast MRI lesion based on multi-classifier

MR-based methods have acceded an important role for the clinical detection and diagnosis of breast cancer. Dynamic contrast-enhanced MRI of the breast has become a robust and successful method, especially for the diagnosis of high-risk cases due to its higher sensitivity compared to X-ray mammography. In the clinical setting, the ANN has been widely applied in breast cancer diagnosis using a subjective impression of different features based on defined criteria. In this study, several neural networks classifiers like MLP, PNN, GRNN, and RBF has been presented on a total of 112 histopathologically verified breast lesions to classify into benign and malignant groups. Also, support vector machine has been considered as classifier. Before applying classification methods, feature selection has been utilized to choose the significant features for classification. Finally, to improve the performance of classification, three classifiers that have the best results among all applied methods have been combined together that they been named as multi-classifier system. For each lesion, final detection as malignant or benign has been evaluated, when the same results have been achieved from two classifiers of multi-classifier system. Tables of results show that the proposed methods are correctly capable to feature selection and improve classification of breast cancer.