Anomaly detection of fundus images

Abstract

Research states that at least 2.2 billion people have a vision impairment or blindness all over the world. There are many reasons for blindness and few of them are leading causes such as cataract, macular degeneration due to age factor, glaucoma, diabetic retinopathy, corneal opacity, trachoma. In all of them, glaucoma is one of the main causes of blindness. Glaucoma is asymptomatic and non-reversible vision loss disease. This paper presents a method of early detection of glaucoma using deep Neural Network (NN) from the retinal images. In different retinal imaging modalities, fundus images are widely accepted. In deep learning, Convolution Neural Network (CNN) is used for feature extraction from the fundus image, and fully connected feed forward NN (FFNN) is used to find out the level of glaucoma. Typical image processing algorithms are used for feature extraction from fundus images and classified with FFNN. The accuracy is compared among different architectures. The TensorFlow software tool and python language are used for this research.