Public databases for glaucoma studies contain color images of the retina, emphasizing the
optic papilla. These databases are intended for research and standardized automated methodologies
such as those using deep learning techniques. These techniques are used to solve complex problems in
medical imaging, particularly in the automated screening of glaucomatous disease. The development
of deep learning techniques has demonstrated potential for implementing protocols for large-scale
glaucoma screening in the population, eliminating possible diagnostic doubts among specialists,
and benefiting early treatment to delay the onset of blindness. However, the images are obtained
by different cameras, in distinct locations, and from various population groups and are centered on
multiple parts of the retina. We can also cite the small number of data, the lack of segmentation of the
optic papillae, and the excavation. This work is intended to offer contributions to the structure and
presentation of public databases used in the automated screening of glaucomatous papillae, adding
relevant information from a medical point of view. The gold standard public databases present
images with segmentations of the disc and cupping made by experts and division between training
and test groups, serving as a reference for use in deep learning architectures. However, the data
offered are not interchangeable. The quality and presentation of images are heterogeneous. Moreover,
the databases use different criteria for binary classification with and without glaucoma, do not offer
simultaneous pictures of the two eyes, and do not contain elements for early diagnosis.