Glaucoma is a persistent, permanent eye disease that contributes to vision and quality of life loss. Within this paper we build a deep learning system for the automatic diagnosis of glaucoma with a Convolutionary neural network. Deep learning algorithms, such as CNNs, that infer a hierarchical representation of images to differentiate between glaucoma and NG trends of diagnostic decisions. The DL architecture proposed contains six learning strategies: four Convolutionary strata and two entirely linked layers. Strategies for drop-out and data rise were implemented to further enhance the treatment of glaucoma. Extensive validation of ORIGA and SCES databases is carried out. The findings show that the recipient's operating curve field under curve (AUC) is significantly higher than the state of the art algorithms in glaucoma identification at 0,831 and 0,887 in the two databases. The method may be used for the detection of glaucoma.