Pterygium is a growth on the eye that can cause blindness, with countries closer to the equator showing higher rate of incidence. However, there is a lack of research to study the severity and properties of the tissue. We propose the use of Generalized Neural Network (GRNN) to objectively quantify redness of the fibrovascular tissue. Comparative analysis using multiple feature selection algorithms indicates that error can be minimized when use with optimal set of features and suitable GRNN spread parameter. Features nominated by Minimum Redundancy Maximum Relevance gives the best performance with SSE = 3.55 and GRNN spread = 0.47.
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
Tel.: +1 703 830 6300
Fax: +1 703 830 2300 email@example.com
(Corporate matters and books only) IOS Press c/o Accucoms US, Inc.
For North America Sales and Customer Service
West Point Commons
Lansdale PA 19446
Tel.: +1 866 855 8967
Fax: +1 215 660 5042 firstname.lastname@example.org