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 task.
Keywords - Multimodal, Machine Learning, Convolutional Neural Networks, Image Classification, Glaucoma.