  Objective: Optical coherence tomography (OCT) and its angiography (OCTA) have
several advantages for the early detection and diagnosis of diabetic
retinopathy (DR). However, automated, complete DR classification frameworks
based on both OCT and OCTA data have not been proposed. In this study, a
convolutional neural network (CNN) based method is proposed to fulfill a DR
classification framework using en face OCT and OCTA. Methods: A densely and
continuously connected neural network with adaptive rate dropout (DcardNet) is
designed for the DR classification. In addition, adaptive label smoothing was
proposed and used to suppress overfitting. Three separate classification levels
are generated for each case based on the International Clinical Diabetic
Retinopathy scale. At the highest level the network classifies scans as
referable or non-referable for DR. The second level classifies the eye as
non-DR, non-proliferative DR (NPDR), or proliferative DR (PDR). The last level
classifies the case as no DR, mild and moderate NPDR, severe NPDR, and PDR.
Results: We used 10-fold cross-validation with 10% of the data to assess the
networks performance. The overall classification accuracies of the three levels
were 95.7%, 85.0%, and 71.0% respectively. Conclusion/Significance: A reliable,
sensitive and specific automated classification framework for referral to an
ophthalmologist can be a key technology for reducing vision loss related to DR.
