  Diabetes is a globally prevalent disease that can cause visible microvascular
complications such as diabetic retinopathy and macular edema in the human eye
retina, the images of which are today used for manual disease screening. This
labor-intensive task could greatly benefit from automatic detection using deep
learning technique. Here we present a deep learning system that identifies
referable diabetic retinopathy comparably or better than presented in the
previous studies, although we use only a small fraction of images (<1/4) in
training but are aided with higher image resolutions. We also provide novel
results for five different screening and clinical grading systems for diabetic
retinopathy and macular edema classification, including results for accurately
classifying images according to clinical five-grade diabetic retinopathy and
four-grade diabetic macular edema scales. These results suggest, that a deep
learning system could increase the cost-effectiveness of screening while
attaining higher than recommended performance, and that the system could be
applied in clinical examinations requiring finer grading.
