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Objective: Optical coherence tomography (OCT) and its angiography (OCTA) haveseveral advantages for the early detection and diagnosis of diabeticretinopathy (DR). However, automated, complete DR classification frameworksbased on both OCT and OCTA data have not been proposed. In this study, aconvolutional neural network (CNN) based method is proposed to fulfill a DRclassification framework using en face OCT and OCTA. Methods: A densely andcontinuously connected neural network with adaptive rate dropout (DcardNet) isdesigned for the DR classification. In addition, adaptive label smoothing wasproposed and used to suppress overfitting. Three separate classification levelsare generated for each case based on the International Clinical DiabeticRetinopathy scale. At the highest level the network classifies scans asreferable or non-referable for DR. The second level classifies the eye asnon-DR, non-proliferative DR (NPDR), or proliferative DR (PDR). The last levelclassifies 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 thenetworks performance. The overall classification accuracies of the three levelswere 95.7%, 85.0%, and 71.0% respectively. Conclusion/Significance: A reliable,sensitive and specific automated classification framework for referral to anophthalmologist can be a key technology for reducing vision loss related to DR.

DcardNet: Diabetic Retinopathy Classification at Multiple Levels Based on Structural and Angiographic Optical Coherence Tomography