Vision is an extremely important sense and its care is vital for the prevention of diseases that can end in irreversible blindness. The disease discussed in this paper is glaucoma, which has a worldwide incidence and it is ranked in the first places in causes of blindness. To contribute to the screening of people who can be suffering from this disease, this work aims to support ophthalmologists by means of an automated algorithm that combines preprocessing of images of the fundus of the eye with mimetic anisotropic filtering, and regression through convolutional neural networks. In this work we demonstrate that by doing the preprocessing with the mimetic anisotropic filtering before passing the images to the convolutional neural networks, we obtain an increase of 1.5% in precision and an increase of 3.27% in sensitivity.