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Inter-Grader Reliability of a Supervised Pterygium Redness Grading System

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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.
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Keywords: Image Processing; Inter-Grader Reliability; Supervised Learning

Document Type: Research Article

Affiliations: 1: Kulliyyah of Allied Health Sciences, International Islamic University Malaysia, Kuantan 25200, Malaysia 2: Kulliyyah of ICT, International Islamic University Malaysia, Gombak 53100, Malaysia 3: Kulliyyah of Medicine, International Islamic University Malaysia, Kuantan 25200, Malaysia

Publication date: October 1, 2016

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  • ADVANCED SCIENCE LETTERS is an international peer-reviewed journal with a very wide-ranging coverage, consolidates research activities in all areas of (1) Physical Sciences, (2) Biological Sciences, (3) Mathematical Sciences, (4) Engineering, (5) Computer and Information Sciences, and (6) Geosciences to publish original short communications, full research papers and timely brief (mini) reviews with authors photo and biography encompassing the basic and applied research and current developments in educational aspects of these scientific areas.
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