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Authors
Affiliations
1 IFET College of Engineering, IN
Source
Biometrics and Bioinformatics, Vol 5, No 7 (2013), Pagination: 271-275
Abstract
The World Health Organization estimates that 135 million people have diabetes mellitus worldwide and that the number of people with diabetes will increase to 300 million by the year 2025.A great effort of the research community is geared towards the creation of an automatic screening system able to promptly detect diabetic retinopathy with the use of fundus cameras. The key for low cost widespread screening is a system usable by operators with little training. In this proposed project we aimed for automatic screening of fundus (retinal) images for detection of diabetic retinopathy using its spatial features and classifying the images using an artificial neural network. Automatic screening will help for the doctors to quickly identify the condition of the patient with more accurate way. Automatic screening system will detect and identify precisely the size of Exudates, microaneursyms, Neovasculatures etc. from the fundus images. This information is fed into an artificial neural network based classifier to identify Diabetic Retinopathy level of the patients. Early detection can potentially reduce the risk of blindness.
Keywords
Diabetic Retinopathy, Microaneursym, Exudates, Fundus Image.
