A novel opposition-based classifier for mass diagnosis in mammography images

In this paper, a novel opposition-based classifier has been developed which classifies breast masses into benign and malignant categories. An MLP network with a novel learning rule, called Opposite Weighted Back Propagation (OWBP), has been utilized as the classifier. The objective is increasing the convergence rate of MLP learning rules as well as improving the mass diagnostic performance. The input ROI, which is a suspected part of mammogram, is segmented manually by expert radiologists and subjected to some preprocessing stages such as histogram equalization, translation and scaling. Then, a group of features which are appropriate descriptors of mass shape, margin and density have been extracted from the preprocessed ROIs. The proposed features include circularity, Zernike moments, contrast, average gray level, NRL derivatives and SP. The proposed classifier has been trained with both traditional BP and OWBP learning rules and the performance have been evaluated. The system which utilizes OWPB learning rule yields a significantly faster training time than BP algorithm while the Az of the resulting CADx system is 0.944.