سال انتشار: ۱۳۸۶

محل انتشار: چهاردهمین کنفرانس مهندسی پزشکی ایران

تعداد صفحات: ۵

نویسنده(ها):

Hossein Parsaei – Systems Design Engineering Department, University of Waterloo, Waterloo, ON, Canada.
Mohammad H.Moradi – Biomedical Engineering School, Amir Kabir University of Technology, Tehran, Iran.
Roya Parsaei – Mechanical Engineering School, Khajeh Nasir Toosi University of Technology, Tehran, Iran.

چکیده:

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.