Ocular Disease Intelligent Recognition

Abstract

To record anatomical details of the eye and anomalies, fundus imaging has proved very efficient. The most effective way to see and diagnose a wide range of eye diseases is through fundus imaging. Conditions that affect the blood vessels and areas surrounding it include diabetes-related retinopathy, glaucoma, AMD, myopia, cataract and hypertension. It's possible for the patient to have more than one ophthalmological problems that can be seen in one or both of his eyes. The dataset provided by ODIR is used in this study. The data has eight different categories for the diseases to be detected. By using transfer learning, two simultaneous models are described for solving the multi label problem for both the eyes (left and right). For the convolutional network, two synchronous efficient net models are implemented which are used with ADAM optimizers for better detection and results outcome. On the ODIR data set, B7 Efficient net along with focal loss outperformed the other approaches with an accuracy rate of 0.96%.