Continuous Eye Disease Severity Evaluation System using Siamese Neural Networks

Abstract

Evaluating the severity of eye diseases using medical images is a very essential and routine task performed in medical diagnosis and treatment. Current grading systems which are largely based on discrete classification are unreliable and do reflect not the entire spectrum of eye disease severity. The unreliability of discrete classification systems for eye diseases is clear, as classification is subjective and done based on the personal opinion of various medical experts, which may vary. In a bid to solve these issues, this study proposes a system for determining the severity of eye diseases on a continuous range using a twin-convoluted neural network approach known as Siamese Neural Networks. This system is demonstrated in the domain of diabetic retinopathy. Samples of retinal fundus images from an eye clinic in India are taken as test cases to evaluate the performance of a Siamese Triplet network which attempts to find the distance between their image embedding. The outputs of the Siamese network when a reference image is juxtaposed with a collection of images with distant severity categories (negative images), as well as when two reference images are compared to each other, are found to have a positive correlation (95\%) with originally assigned severity classes. Hence, these outputs indicate a continuous range of the severity and change in eye diseases.