Glaucoma Diagnosis using Cooperative Convolutional Neural Networks
Glaucoma is the most common optic neuropathy characterized by normal to raised intraocular pressure (IOP),
visual field defects, loss of retinal nerve fiber layer, thinning of the neuroretinal rim, and cupping of the optic disc. Machine
learning for glaucoma diagnosis has achieved great development in recent years. In machine learning domain, learning using
multimodal data has attracted much attention due to its superior performance. For instance, for the diagnosis of disease.
In this paper, we propose a convolutional neural networks (CNN) approach to diagnosing glaucoma using multimodal data
from retinal fundus images and achieve high classification accuracy. We develop a network with CNN architecture that avoid
the classical handcrafted features extraction step, by processing features extraction and classification at one time within the
same network of neurons and consequently provide a diagnosis automatically and without user input. We train this network
on the publicly available RIM-ONE dataset and demonstrate impressive results, particularly for a high-level classification
Keywords - Multimodal, Machine Learning, Convolutional Neural Networks, Image Classification, Glaucoma.