Automatic and accurate classification of retinal optical coherence tomography (OCT) images is essential to assist ophthalmologists in the diagnosis and grading of macular diseases. Most existing methods classify 3-D retinal OCT volumes by separately analyzing each single-frame 2-D B-scan, and thus inevitably ignore significant temporal information among B-scans. In this paper, we propose to classify volumetric OCT images via a recurrent neural network (VOCT-RNN) which can fully exploit temporal information among B-scans. Specifically, a deep co...
