APPLICATION OF NEURAL NETWORKS IN AID FOR DIAGNOSIS FOR PATIENTS WITH GLAUCOMA

Glaucoma is an ophthalmologic disease very difficult to diagnose in the earlier phase. Additionally, exams and methods used to give reliable information for correct diagnosis are usually very expensive. Therefore, other methods less expensive and also reliable must be proposed as an auxiliary tool to Glaucoma diagnosis. This paper analyzes the performance of neural networks as an auxiliary tool for the diagnosis of patients with glaucoma, avoiding the use of data only available in expensive exams. The analysis considers two different kinds of neural networks (Multi-Layer Perceptron (MLP) and Probabilistic Neural Networks (PNN)) and two different methods variable selection: a random and iterative method; and the Least Square Extrapolation (LSE) method. The paper also evaluates the benefits of applying principal components analysis (PCA) to the database. The results obtained were very good, attaining an accuracy of more than 90% of correct classification of all cases present in our database. It confirms the real possibility of using neural networks as an auxiliary and inexpensive tool to help in Glaucoma diagnosis.