Abstract
Nowadays, classification is the most efficient method for breast cancer detection using mammography images. During the last two decades, researchers achieved very good results using different kinds of classification methods. In this context, we will focus in this work on the quality of mammography image classification by proposing a new approach that us compares with the state-of-art methods. Our method consists of two phases; a semi-supervised step based on SKDA and a second step based on SVM. As will be shown in the experiments section, results on Mammography data set show that the proposed algorithm can get very good results. The application of our algorithm on five well known real data sets allow us to validate our method and show its interest in the context of image based medical diagnosis. The best precision obtained by NSVC exceeded 99 %.
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