Open AccessAnomaly detection of fundus images
Author(s) -
Shalaka Jagtap,
John Sahaya Rani Alex
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1716/1/012044
Subject(s) - glaucoma , fundus (uterus) , convolutional neural network , computer science , artificial intelligence , feature extraction , deep learning , diabetic retinopathy , macular degeneration , ophthalmology , optometry , medicine , computer vision , diabetes mellitus , endocrinology
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.