Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Diabetic Retinopathy Diagnostics from Retinal Images based on Deep Convolutional Networks

Version 1 : Received: 30 May 2020 / Approved: 31 May 2020 / Online: 31 May 2020 (18:55:43 CEST)

How to cite: Sampaul Thomas, G.A.; Robinson, Y.H.; Julie, E.G.; Shanmuganathan, V.; Nam, Y.; Rho, S. Diabetic Retinopathy Diagnostics from Retinal Images based on Deep Convolutional Networks. Preprints 2020, 2020050493 (doi: 10.20944/preprints202005.0493.v1). Sampaul Thomas, G.A.; Robinson, Y.H.; Julie, E.G.; Shanmuganathan, V.; Nam, Y.; Rho, S. Diabetic Retinopathy Diagnostics from Retinal Images based on Deep Convolutional Networks. Preprints 2020, 2020050493 (doi: 10.20944/preprints202005.0493.v1).

Abstract

Retinopathy is a human eye disease that causes changes in retinal blood vessels that leads to bleed, leak fluid and vision impairment. Symptoms of retinopathy are blurred vision, changes in color perception, red spots, and eye pain. In this paper, a new methodology based on Convolutional Neural Networks (CNN) is developed and proposed to diagnose and give a decision about the presence of retinopathy. The CNN model is trained by different images of eyes that have retinopathy and those which do not have retinopathy. The performance of the proposed model is compared with the related methods of DREAM, KNN, GD-CNN and SVM. Experimental results show that the proposed CNN performs better.

Subject Areas

Convolutional Neural Networks; Dental Diagnosis; Image Recognition; Diabetic Retinopathy detection

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