  In this paper, we propose a Computer Assisted Diagnosis (CAD) system based on
a deep Convolutional Neural Network (CNN) model, to build an end-to-end
learning process that classifies breast mass lesions. We investigate the impact
that has transfer learning when large data is scarce, and explore the proper
way to fine-tune the layers to learn features that are more specific to the new
data. The proposed approach showed better performance compared to other
proposals that classified the same dataset.
