Computer-Based Classification of Eye Diseases

• Published in: 2006 International Conference of the IEEE Engineering in Medicine and Biology Society Vol. 2006; pp. 6121 - 6124
• Main Authors: Acharya, U.R., Kannathal, N., Ng, E.Y.K., Min, L.C., Suri, J.S.
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 2006
• Subjects: Analysis of Variance; Arcus Senilis - diagnosis; Artificial neural networks; cataract; Cataract - diagnosis; classification; Competitive intelligence; Cornea; corneal haze; Databases, Factual; Decision Support Techniques; Diagnosis, Computer-Assisted - methods; Diseases; Eye; Eye Diseases - classification; Eye Diseases - therapy; features; fuzzy classifier; Fuzzy Logic; Glaucoma - diagnosis; Humans; image processing; Intelligent systems; iridocyclitis; Iris; neural networks; Neural Networks, Computer; neuro-fuzzy classifier ANOVA test; normal eye; Ophthalmology - instrumentation; Pathology; Protocols; Reproducibility of Results; Senior citizens; sensitivity; Signal Processing, Computer-Assisted; Software; specificity; Surgery
• ISBN: 9781424400324; 1424400325
• ISSN: 1557-170X
• DOI: 10.1109/IEMBS.2006.260211

Eye disorders among the elderly are a major health problem. With advancing age, the normal function of eye tissues decreases and there is an increased incidence of ocular pathology. The most common causes of age related eye disorder and visual impairment in the elderly are cataracts, iridocyclitis and corneal haze. Iridocyclitis is an inflammation of the iris (the colored part of the eye), while corneal haze is a complication of refractive surgery characterized by the cloudiness of the normally clear cornea. Computer-based intelligent system for classification of these eye diseases is very useful in diagnostics and disease management. This paper presents a comparison of three classification strategies to classify four kinds of eye data sets (three different kinds of eye diseases and a normal class). Our protocol uses three different kinds of classifiers: artificial neural network, fuzzy classifier and neuro-fuzzy classifier. Features are extracted from these raw images which are then fed to these classifiers. These classifiers are run on a database of 135 subjects using the cross-validation strategy. We demonstrate a sensitivity of more than 85% for these classifiers with the specificity of 100% and results are very promising