Feature Selection and Classification of Breast Cancer on Dynamic Magnetic Resonance Imaging Using ANN and SVM

TLDR: In this paper, feature selection and classification methods based on Artificial Neural Network (ANN) and Support Vector Machine (SVM) are applied to classify breast cancer on dynamic Magnetic Resonance Imaging (MRI).

Abstract: Breast cancer Dynamic magnetic resonance imaging (MRI) has emerged as a powerful diagnostic tool for breast cancer detection due to its high sensitivity and has established a role where findings from conventional mammography techniques are equivocal[1]. 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, feature selection and classification methods based on Artificial Neural Network (ANN) and Support Vector Machine (SVM) are applied to classify breast cancer on dynamic Magnetic Resonance Imaging (MRI). The database including benign and malignant lesions is specified to select the features and classify with proposed methods. It was collected from 2004 to 2006. A forward selection method is applied to find the best features for classification. Moreover, 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 have been considered as classifiers. Training and recalling classifiers are obtained with considering four-fold cross validation.