A mass classification and image retrieval model for mammograms

Abstract

In this paper, a breast tissue density classification and image retrieval model is studied and a model for the data reduction is presented. This model is based on two-directional two-dimensional principal component analysis ((2D)2PCA) technique, and a support vector machine (SVM) with the radial basis function (RBF) for mammographic images classification and retrieval. The model is formed based on breast density, according to the categories defined by the breast imaging-reporting and data system (BIRADS) which is a standard on the assessment of mammographic images and is tested on the Mammographic Image Analysis Society (MIAS) database. The five-fold cross-validation has been used for the parameters selection in SVM to avoid the over-fitting error in the data classification. The average precision rates of the model are in the range from 87·34% to 99·12%.