Detection and classification of Non-Proliferative Diabetic Retinopathy using a Back-Propagation neural network
Keywords:early diagnosis, automatically identification, fundus images, diabetic Retinopathy, diabetic retinopathy
One of the most serious complications of type 2 Diabetes Mellitus (DM) is the Diabetic Retinopathy (DR). DR is a silent disease and is only recognized when the changes on the retina have progressed to a level at which treatment turns complicate, so an early diagnosis and referral to an ophthalmologist or optometrist for the management of this disease can prevent 98% of severe visual loss. The aim of this work is to automatically identify Non Diabetic Retinopathy (NDR), and Background Retinopathy using fundus images. Our results show a classification accuracy of 92%, with sensitivity and specifity of 95%.
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