Nayak, J.Subbanna Bhat, P.S.Acharya, R.2026-02-052009Journal of Medical Engineering and Technology, 2009, 33, 2, pp. 119-1293091902https://doi.org/10.1080/03091900701349602https://idr.nitk.ac.in/handle/123456789/27663Diabetes mellitus is a major cause of visual impairment and blindness. Twenty years after the onset of diabetes, almost all patients with type 1 diabetes and over 60% of patients with type 2 diabetes will have some degree of retinopathy. Prolonged diabetes retinopathy leads to maculopathy, which impairs the normal vision depending on the severity of damage of the macula. This paper presents a computer-based intelligent system for the identification of clinically significant maculopathy, non-clinically significant maculopathy and normal fundus eye images. Features are extracted from these raw fundus images which are then fed to the classifier. Our protocol uses feed-forward architecture in an artificial neural network classifier for classification of different stages. Three different kinds of eye disease conditions were tested in 350 subjects. We demonstrated a sensitivity of more than 95% for these classifiers with a specificity of 100%, and results are very promising. Our systems are ready to run clinically on large amounts of datasets. © 2009 Informa Healthcare USA, Inc.ClosingFeed-forwardFundusMaculopathyOpeningRetinopathyAutomationClassifiersElectronic data interchangeEye protectionImage processingIntelligent systemsLearning systemsMedical problemsNetwork architectureSugar (sucrose)Neural networksAgedAged, 80 and overAnalysis of VarianceDiabetic RetinopathyDiagnostic ImagingExudates and TransudatesFemaleFovea CentralisFundus OculiHumansImage EnhancementMacula LuteaMaleMiddle AgedNeural Networks (Computer)Normal DistributionPattern Recognition, AutomatedPhotographyPredictive Value of TestsSensitivity and Specificity