A methodology for classification of lesions in mammographies using Zernike Moments, ELM and SVM Neural Networks in a multi-kernel approach

The WHO (World Health Organization) estimates that, in 2012, it will emerge 1.7 million new cases of breast cancer in world. Many studies aim to distinguish malignant cancers from benign. The goal of the proposed work is give to health professional more subsidies in order to analyze the patient situation, through the tumor contour classification. The lesion contour is a predominant factor in order to choose the appropriate treatment for the patient and detecting the degree of malignancy of the cancer. The proposed work classifies the lesion according the American College of Radiology rules. It is employed two groups of Zernike Moments in order to descript the tumor contour and applied to ELM and SVM Neural Networks. Different from the ELM and SVM in literature, the proposed work extends these two neural networks to kernel learning. The best result is about 80% of hit rate, using SVM with a RBF kernel.