InceptionCaps: A Performant Glaucoma Classification Model for
  Data-scarce Environment
Gyanendar Manohar Ruairi O'Reilly
Glaucoma is an irreversible ocular disease and is the second leading cause of
visual disability worldwide. Slow vision loss and the asymptomatic nature of
the disease make its diagnosis challenging. Early detection is crucial for
preventing irreversible blindness. Ophthalmologists primarily use retinal
fundus images as a non-invasive screening method. Convolutional neural networks
(CNN) have demonstrated high accuracy in the classification of medical images.
Nevertheless, CNN's translation-invariant nature and inability to handle the
part-whole relationship between objects make its direct application unsuitable
for glaucomatous fundus image classification, as it requires a large number of
labelled images for training. This work reviews existing state of the art
models and proposes InceptionCaps, a novel capsule network (CapsNet) based deep
learning model having pre-trained InceptionV3 as its convolution base, for
automatic glaucoma classification. InceptionCaps achieved an accuracy of 0.956,
specificity of 0.96, and AUC of 0.9556, which surpasses several
state-of-the-art deep learning model performances on the RIM-ONE v2 dataset.
The obtained result demonstrates the robustness of the proposed deep learning
model.