Classification of glaucoma Optical Coherence Tomography (OCT) images based on blood vessel identification using CNN and firefly optimization

Abstract

Glaucoma is a chronic ocular neurodegenerative condition characterised by optic neuropathy and visual disturbance, corresponding to optic disc cupping and degeneration of optic nerve fibres. Globally 76 million people are suffered from glaucoma, it is an aggregate name of a gathering of eye conditions which can cause vision loss and in the end bring about visual deficiency by dynamic basic and useful harm to the optic nerve. It is one of the main reasons for visual deficiency. Early detection of this condition can reduce the progression of the disease and saves many users throughout the world. The detection and identification of glaucoma in an image are important for controlling the loss of the vision. Even there are numerous models for classification of glaucoma disease, the effected rate and prediction rate is less. But accurate identification is a foremost important thing. In order to train CNN, data augmentation and dropout were performed. A classifier was trained to identify the disc fundus images of healthy and glaucomatous eyes using feature vector representation of each input image to integrate the results from each CNN model, removing the second completely linked layer. In this manuscript convolutional neural network (CNN) is proposed with an optimization mechanism for classification of glaucoma OCT images using CNN based firefly optimization model. The proposed firefly based CNN model performs better when compared to the state of art mechanisms.