  Early detection of diabetic retinopathy prevents visual loss and blindness of
a human eye. Based on the types of feature extraction method used, DR detection
method can be broadly classified as Deep Convolutional Neural Network (CNN)
based and traditional feature extraction (machine learning) based. This paper
presents a comprehensive survey of existing feature extraction methods based on
Deep CNN and conventional feature extraction for DR detection. In addition to
that, this paper focuses on the severity scale measurement of the DR detection
and to the best of our knowledge this is the first survey paper which covers
severity grading scale. It is also necessary to mention that this is the first
study which reviews the proposed Deep CNN based method in the state of the art
for DR detection methods. This study discovers that recently proposed deep
learning based DR detection methods provides higher accuracy than existing
traditional feature extraction methods in the literature and also useful in
large scale datasets. However, deep learning based methods require GPU
implementation to get the desirable output. The one of the other major finding
of this paper is that there are no obvious standard severity scale detection
criteria to measure the grading. Some used binary class while many other used
multi stage class.
