Diagnosis of Ophthalmic Diseases in Fundus Image by Using Deep Learning

Abstract

Abstract: In the human eye, Damage in the retina may cause ophthalmic diseases like cataracts, AMD, Hypertensive retinopathy, myopia, etc. To cure these diseases, many ophthalmologists use retinal fundus images as an important information source to find out ophthalmic diseases. Multiple techniques have been introduced for the screening of ocular diseases. Today’s world is in great demand to find out ocular diseases by using deep learning and machine learning techniques. This paper uses pre-trained deep neural networks to determine five categories of ophthalmic diseases such as cataract, AMD, Hypertensive retinopathy, myopia, and normal. Dataset is created into binary and multiclass, then trained on Resnet-101 of convolutional neural network (CNN) and evaluated. The accuracy of this model is found to be 90.38% and 88.5% for binary and multiclass respectively. Keywords: Retinal fundus image, Ocular diseases, CNN, ResNet, deep learning. Image processing, Ensemble classifier