Analysing visual field and diagnosing glaucoma progression using a hybrid of per location differences and artificial neural network ensembles

Visual function test results for glaucoma diagnosis is perceived to be subjective and problematic.In this paper, we aim to address the issues and problems associated with these current approaches.We present (a) a system architecture for analyzing visual field and diagnosing glaucoma progression; (b) a per location differences approach for analyzing visual field to obtain measurements of glaucoma progression; and (c) a neural network ensemble approach where several artifial neural network are jointly used to diagnose glaucoma progression.It is hoped that it would be possible to diagnose glaucoma progression with just one reading of a patient’s visual field

An application of machine learning techniques for the classification of glaucomatous progression

This paper presents an application of machine learning to the problem of classifying patients with glaucoma into one of two classes:stable and progressive glaucoma. The novelty of the work is the use of new features for the data analysis combined with machine learning techniques to classify the medical data. The paper describes the new features and the results of using decision trees to separate stable and progressive cases. Furthermore, we show the results of using an incremental learning algorithm for tracking stable and progressive cases over time. In both cases we used a dataset of progressive and stable glaucoma patients obtained from a glaucoma clinic.<br /