Progress on combining OCT-A with deep learning for diabetic retinopathy diagnosis

We present novel approaches of implementing state-of-the-art deep learning techniques for the processing of optical coherence tomography angiography (OCT-A) images for the classification of diabetic retinopathy (DR) severity. The effects of feature-engineering on a deep neural network’s classification performance is compared against unprocessed OCT-A images. We investigate the effects of lower axial resolution (simulated by using a narrower spectral bandwidth) on the classification of DR severity, and the recovery of lost features using a generative adversarial network. We also explore the relationship between DR severity classification and lateral resolution.