  Objective: Ophthalmological pathologies such as glaucoma, diabetic
retinopathy and age-related macular degeneration are major causes of blindness
and vision impairment. There is a need for novel decision support tools that
can simplify and speed up the diagnosis of these pathologies. A key step in
this process is to automatically estimate the quality of the fundus images to
make sure these are interpretable by a human operator or a machine learning
model. We present a novel fundus image quality scale and deep learning (DL)
model that can estimate fundus image quality relative to this new scale.
  Methods: A total of 1,245 images were graded for quality by two
ophthalmologists within the range 1-10, with a resolution of 0.5. A DL
regression model was trained for fundus image quality assessment. The
architecture used was Inception-V3. The model was developed using a total of
89,947 images from 6 databases, of which 1,245 were labeled by the specialists
and the remaining 88,702 images were used for pre-training and semi-supervised
learning. The final DL model was evaluated on an internal test set (n=209) as
well as an external test set (n=194).
  Results: The final DL model, denoted FundusQ-Net, achieved a mean absolute
error of 0.61 (0.54-0.68) on the internal test set. When evaluated as a binary
classification model on the public DRIMDB database as an external test set the
model obtained an accuracy of 99%.
  Significance: the proposed algorithm provides a new robust tool for automated
quality grading of fundus images.
