  Purpose To develop a computer based method for the automated assessment of
image quality in the context of diabetic retinopathy (DR) to guide the
photographer.
  Methods A deep learning framework was trained to grade the images
automatically. A large representative set of 7000 color fundus images were used
for the experiment which were obtained from the EyePACS that were made
available by the California Healthcare Foundation. Three retinal image analysis
experts were employed to categorize these images into Accept and Reject classes
based on the precise definition of image quality in the context of DR. A deep
learning framework was trained using 3428 images.
  Results A total of 3572 images were used for the evaluation of the proposed
method. The method shows an accuracy of 100% to successfully categorise Accept
and Reject images.
  Conclusion Image quality is an essential prerequisite for the grading of DR.
In this paper we have proposed a deep learning based automated image quality
assessment method in the context of DR. The method can be easily incorporated
with the fundus image capturing system and thus can guide the photographer
whether a recapture is necessary or not.
