Аннотация:
Diabetic retinopathy is one of the most threatening complications of diabetes that leads to permanent blindness
if left untreated. One of the essential challenges is early detection, which is very important for treatment
success. Unfortunately, the exact identification of the diabetic retinopathy stage is notoriously tricky and
requires expert human interpretation of fundus images. Simplification of the detection step is crucial and
can help millions of people. Convolutional neural networks (CNN) have been successfully applied in many
adjacent subjects, and for diagnosis of diabetic retinopathy itself. However, the high cost of big labeled
datasets, as well as inconsistency between different doctors, impede the performance of these methods. In
this paper, we propose an automatic deep-learning-based method for stage detection of diabetic retinopathy
by single photography of the human fundus. Additionally, we propose the multistage approach to transfer
learning, which makes use of similar datasets with different labeling. The presented method can be used
as a screening method for early detection of diabetic retinopathy with sensitivity and specificity of 0.99 and
is ranked 54 of 2943 competing methods (quadratic weighted kappa score of 0.925466) on APTOS 2019
Blindness Detection Dataset (13000 images).
