  Deep learning models have the capacity to fundamentally revolutionize medical
imaging analysis, and they have particularly interesting applications in
computer-aided diagnosis. We attempt to use deep learning neural networks to
diagnose funduscopic images, visual representations of the interior of the eye.
Recently, a few robust deep learning approaches have performed binary
classification to infer the presence of a specific ocular disease, such as
glaucoma or diabetic retinopathy. In an effort to broaden the applications of
computer-aided ocular disease diagnosis, we propose a unifying model for
disease classification: low-cost inference of a fundus image to determine
whether it is healthy or diseased. To achieve this, we use transfer learning
techniques, which retain the more overarching capabilities of a pre-trained
base architecture but can adapt to another dataset. For comparisons, we then
develop a custom heuristic equation and evaluation metric ranking system to
determine the optimal base architecture and hyperparameters. The Xception base
architecture, Adam optimizer, and mean squared error loss function perform
best, achieving 90% accuracy, 94% sensitivity, and 86% specificity. For
additional ease of use, we contain the model in a web interface whose file
chooser can access the local filesystem, allowing for use on any
internet-connected device: mobile, PC, or otherwise.
