Optimal Transfer Learning Model for Binary Classification of Funduscopic Images through Simple Heuristics
share
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 diagnose fundus eye exams, visual representations of the eye's interior. Recently, a few 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. We use transfer learning models, comparing their "base" architectures and hyperparameters via. a custom heuristic and evaluation metric ranking system. The Xception base model, Adam optimizer, and mean squared error loss function perform best, achieving 90 accuracy, 94
READ FULL TEXT
