Fuzzy Morphological Extreme Learning Machines to detect and classify masses in mammograms

According to the World Health Organization, breast cancer is the most common type of cancer in women. It is also the second leading cause of death among women around the world, becoming the most fatal form of cancer. However, to detect and classify masses is a hard task even for experts. Therefore, due to medical experience, different diagnoses to an image are commonly found. The use of a computer assisted diagnosis is important to avoid misdiagnoses. In this work, we propose Fuzzy Morphological Extreme Learning Machines, with hidden layer kernel based on nonlinear morphological operators of erosion and dilation. The proposed methods were evaluated using 2.796 images from IRMA database, considering fat, fibroid, dense and extremely dense tissues. Zernike Moments and Haralick texture features are used as image descriptors. The proposed model classifies masses as benign, malignant or normal. Results shows comparison between Extreme Learning Machines using Sigmoid and Fuzzy Morphological Kernels, evaluated using classification rate and Kappa index. When using fuzzy morphological kernels, classification rate and Kappa value increases for most of cases analyzed.