This article addresses breast cancer diagnosis using mammographic images. Throughout, the diagnosis is done using the mammographic microcalcifications. The aim of the work presented here is twofold. First, we introduce a back-end phase, based on machine learning techniques, in a previous computer aided diagnosis system. The two machine learning techniques incorporated are case-based reasoning and genetic algorithms. These algorithms look for improving the results obtained by human experts and the previous statistical model. On the other hand, we analyse the obtained results comparing them with the ones provided by other well-known machine learning techniques. The breast cancer dataset used in the experiments come from Girona Health Area. This database contains 216 images previously diagnosed by surgical biopsy.