Automated Glaucoma Diagnosis Using Deep and Transfer Learning: Proposal of a System for Clinical Testing

We developed a deep learning algorithm for identifying glaucoma on optic nerve head (ONH) photographs. We applied transfer learning to overcome overfitting on the small training sample size that we employed. The transfer learning framework that was previously trained on large datasets such as ImageNet, uses the initial parameters and makes the approach applicable to small sample sizes. We then classified the input ONH photographs as “normal” or “glaucoma”. The proposed approach achieved a validation accuracy of 92.3% on a dataset of 277 ONH photographs from normal eyes and 170 ONH photographs from eyes with glaucoma. In order to re-test the accuracy and generalizability of the proposed approach, we re-tested the algorithm using an independent dataset of 30 ONH photographs. The re-test accuracy was 80.0% on average.