  Cases of diabetes and related diabetic retinopathy (DR) have been increasing
at an alarming rate in modern times. Early detection of DR is an important
problem since it may cause permanent blindness in the late stages. In the last
two decades, many different approaches have been applied in DR detection.
Reviewing academic literature shows that deep neural networks (DNNs) have
become the most preferred approach for DR detection. Among these DNN
approaches, Convolutional Neural Network (CNN) models are the most used ones in
the field of medical image classification. Designing a new CNN architecture is
a tedious and time-consuming approach. Additionally, training an enormous
number of parameters is also a difficult task. Due to this reason, instead of
training CNNs from scratch, using pre-trained models has been suggested in
recent years as transfer learning approach. Accordingly, the present study as a
review focuses on DNN and Transfer Learning based applications of DR detection
considering 38 publications between 2015 and 2020. The published papers are
summarized using 9 figures and 10 tables, giving information about 22
pre-trained CNN models, 12 DR data sets and standard performance metrics.
