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Deep learning models have the capacity to fundamentally revolutionize medicalimaging analysis, and they have particularly interesting applications incomputer-aided diagnosis. We attempt to use deep learning neural networks todiagnose funduscopic images, visual representations of the interior of the eye.Recently, a few robust deep learning approaches have performed binaryclassification to infer the presence of a specific ocular disease, such asglaucoma or diabetic retinopathy. In an effort to broaden the applications ofcomputer-aided ocular disease diagnosis, we propose a unifying model fordisease classification: low-cost inference of a fundus image to determinewhether it is healthy or diseased. To achieve this, we use transfer learningtechniques, which retain the more overarching capabilities of a pre-trainedbase architecture but can adapt to another dataset. For comparisons, we thendevelop a custom heuristic equation and evaluation metric ranking system todetermine the optimal base architecture and hyperparameters. The Xception basearchitecture, Adam optimizer, and mean squared error loss function performbest, achieving 90% accuracy, 94% sensitivity, and 86% specificity. Foradditional ease of use, we contain the model in a web interface whose filechooser can access the local filesystem, allowing for use on anyinternet-connected device: mobile, PC, or otherwise.

Optimal Transfer Learning Model for Binary Classification of Funduscopic Images through Simple Heuristics