Inter-grader reliability of a supervised pterygium redness grading system - Che Azemin, Mohd Zulfaezal; Mohd Tamrin, Mohd Izzuddin; Hilmi, Mohd Radzi; Mohd. Kamal, Khairidzan (American Scientific Publishers, 2016)
This research work outlines the methodology of a medical image grading system based on supervised learning algorithm. A total of 210 features were extracted in various color spaces and most relevant features were identified and fed into a regularized feedforward neural network. The inter-grader reliability of the supervised system was then assessed based on the manual delineation of region of interest by 2 human graders. Intra-class correlation analysis of the experiments shows excellent agreement of 0.869 to 0.954.