Development and Verification of Artificial Neural Network Classifiers for Eye Diseases Diagnosis

Visual field sensitivity test results are crucial for accurate and efficient diagnosis of blinding diseases such as glaucoma, scotoma, homonymous, lesions of the optic nerves, lesions of the chiasm, etc. Typically in computerized perimeters, analysis of visual field sensitivity test results is performed by statistical methods. The purpose of these analyses is to help ascertain whether the test results are acceptable or not and also what the disorder is. Herein, first, Kohonen’s self – organizing map (SOM) is used to establish whether the Perimetry result is reliable or not. Then, Multilayer Perceptron (MLP), Probability Neural Network (PNN), Radial-Basis Function Network (RBFN) and Support Vector Machines (SVM) is used to analyze premetry results. By comparing the statistics, the artificial neural network classifiers show encouraging performance and SVM has the best performance.

Keywords:

Visual field , Standard automated perimetery , perimeter , Multi Layer Perceptron (MLP) , Probability Neural Network (PNN) , Radial-Basis Function Network (RBFN) , Support Vector Machine (SVM)