  Objective: To validate and compare the performance of eight available deep
learning architectures in grading the severity of glaucoma based on color
fundus images. Materials and Methods: We retrospectively collected a dataset of
5978 fundus images and their glaucoma severities were annotated by the
consensus of two experienced ophthalmologists. We preprocessed the images to
generate global and local regions of interest (ROIs), namely the global
field-of-view images and the local disc region images. We then divided the
generated images into three independent sub-groups for training, validation,
and testing purposes. With the datasets, eight convolutional neural networks
(CNNs) (i.e., VGG16, VGG19, ResNet, DenseNet, InceptionV3, InceptionResNet,
Xception, and NASNetMobile) were trained separately to grade glaucoma severity,
and validated quantitatively using the area under the receiver operating
characteristic (ROC) curve and the quadratic kappa score. Results: The CNNs,
except VGG16 and VGG19, achieved average kappa scores of 80.36% and 78.22% when
trained from scratch on global and local ROIs, and 85.29% and 82.72% when
fine-tuned using the pre-trained weights, respectively. VGG16 and VGG19
achieved reasonable accuracy when trained from scratch, but they failed when
using pre-trained weights for global and local ROIs. Among these CNNs, the
DenseNet had the highest classification accuracy (i.e., 75.50%) based on
pre-trained weights when using global ROIs, as compared to 65.50% when using
local ROIs. Conclusion: The experiments demonstrated the feasibility of the
deep learning technology in grading glaucoma severity. In particular, global
field-of-view images contain relatively richer information that may be critical
for glaucoma assessment, suggesting that we should use the entire field-of-view
of a fundus image for training a deep learning network.
