Detection of Keratoconus Disease

Abstract

It has described that the development of an application which is used to detect Kera-toconus disease is based on the photographs of the eyes. At the heart of the application is an algorithm that acquires the features of an eye, then classify the affected and healthy eye. Firstly, It has proposed a basic identification of keratoconus disease using supervised learning. Described method obtains better results in comparison with other existing methods with the same dataset. The algorithm which used in this method is trained against several samples of healthy and affected eyes and then used to classify unknown sample of eyes. This algorithm analyzes the eye’s corneal curvature and topography using a convolutional neural network (CNN), capable of extracting and understanding an eye’s features. This algorithm is very successful in properly classifying images contained in the training library. The aim of this study is to characterize another arrangement technique to recognize keratoconus based on statistical analysis, and to understand the forecast with insightful systems of these organized facts. The purpose of this study is to define another grouping strategy to differentiate keratoconus based on statistical analysis and to understand the expectations.