Breast cancer has a high incidence among women worldwide. This, together with the recent developments in deep learning based convolutional networks, have motivated research towards the enhancement of Computer Aided Diagnosis (CAD) systems. In this paper, the performance of a densely connected convolutional network (DenseNet) for breast cancer was investigated for the malignant/benign classification of mammographic masses. Different mammography data sets were collected to investigate the capacity of this network for learning a combination of these databases. To achieve this, internal low-level, mid-level and high-level features/abstracts were extracted from the model together with hand-crafted features, generating a vast amount of data. Using the distributed rough set based feature selection approach (Sp-RST), significant features were selected from both deep learning based features and hand-crafted ones, and fed into a learning model with separate and combined data approaches for the classification of mammographic masses. Results show that by using Sp-RST as a powerful technique capable of performing big data preprocessing, DenseNet had the representational capacity to learn mammographic abnormalities.