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Research Article

JCR. 2020; 7(11): 2232-2239


DIAGNOSIS OF DIABETIC RETINOPATHY WITH TRANSFER LEARNING FROM DEEP CONVOLUTIONAL NEURAL NETWORK

P.V. Lakshmi, NidhiBartakke, P. Srinivasa Rao, G.A.V. Rama Chandra Rao, D. Srikanth.

Abstract
Diabetic retinopathy is one of the common retinal diseases in diabetic patients that may lead to
causes blindness. Over last few decades there has been major advances in understanding Diabetic
retinopathy(DR), its clinical assessment and diagnosis. DR affects blood vessels of retina that can lead to
diabetic macular edema, Neovascular glaucoma and retinal detachment. Advancement in research gave awareness
in ophthalmological practices to determine enhanced and economical methods of detecting, diagnosis of retinal
disease. This can be accomplished by developing AI based analysis tools. It is time consuming to manually inspect
hemorrhages, blood vessels and other morphological changes from fundus images. To classify DR images into
five classes, we used two Deep learning CNN pretrained models ResNet50 and Xception with different optimizers
and activation functions. Both models showed an accuracy of 83% on test data. The Proposed Deep learning
model can be used to classify the stages of diabetic retinopathy from fundus images.

Key words: Diabetic retinopathy, Neural Networks, Deep learning, Convolution neural network



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