An automated CNN architecture search for glaucoma diagnosis based on NEAT

Glaucoma is an ocular disease that causes damage to the optic nerve, inducing successive narrowing of the visual field in affected patients due to an increased intraocular pressure, which can lead patients to blindness in an advanced stage without clinical reversal. For several years, the use of deep learning with convolutional neural networks (CNNs) has been successfully put into practice for several years. However, building a deep learning network requires an amount of experiments to find the fittest parameters, best choice of layers and an amount of available data. Thus it is not always able to produce satisfactory results due to the amount of parameters that need to be configured to adapt the CNN architecture to the problem in question, in most cases, with small datasets. Based on this scenario, this paper proposes and analyzes a CNN architecture construction from scratch, based on Neuroevolution of Augmenting Topologies for diagnosing glaucoma from fundus images. The method was evaluated with RIM-ONE and the combination of five glaucoma datasets in which we highlight 0.961 and 0.943 of f1-score, respectively for each dataset.