ANALYSIS OF SVM PARAMETRIZATION IN THE CLASSIFICATION OF MAMMOGRAPHIC TEXTURE IMAGES
Studies indicate that breast density is related to the risk of developing cancer since dense breast
tissue can hide lesions, causing cancer to be detected at later stages. In this paper we classication method using support vector machines (SVM) associated to data reduction techniques to classify mammographic texture. An analysis of the parameters that influence the efectiveness of texture classication is also provided. Experiments were conducted on a set of 4,000 mammographic exams from which regions of interest representing the most signicantly part of the texture of the breast tissue were extracted. Compared to other quantitative results found in the literature, the proposed multi-class SVM method using the radial basis function kernel and tuned parameters proved to be superior while classifying mammographic texture, reaching up to 99% of precision for 10% of recall.