InceptionCaps: A Performant Glaucoma Classification Model for Data-scarce Environment

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.