Supervised pterygium fibrovascular redness grading using generalized regression neural network - Che Azemin, Mohd Zulfaezal; Hilmi, Mohd. Radzi; Mohd. Kamal, Khairidzan (IOS Press, 2014)
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.