Abstract
Glaucoma is an optic nerve disease that can also be caused by Diabetes due to the increase in the intraocular pressure. When the disease is not detected in time, it can cause severe vision loss. Through the use of artificial intelligence this process can be automated. In order to reach as many people as possible, mobile technologies can be used for where ophthalmologists are scarce. In this paper, several aspects of Glaucoma detection systems based on Convolutional Neural Networks are examined and analyzed for mobile use in order to discover the key factors for most effective detection systems. Findings demonstrate that dataset variability, dataset size and training time are important factors for mobile use with lighter models based on MobileNet.
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