Publication details
Many eye diseases like cataracts, trachoma, or cornealulcer can cause vision problems. Progression of
these eye diseases can only be prevented if they are recognized accurately at the early stage. Visually
observable symptoms differ a lot among these eye diseases. However, a wide variety of symptoms is
necessary to be analyzed for the accurate detection of eye diseases. In this paper, we propose a novel
approach to provide an automated eye disease recognition system using visually observable symptoms
applying digital image processing techniques and machine learning techniques such as deep
convolution neural network (DCNN) and support vector ma- chine (SVM). We apply the principal
component analysis and distributed stochastic neighbor embedding methods for better feature
selection. The proposed system automatically divides the facial components from the frontal facial
image and extracts the eye part.