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  Diabetic Retinopathy Detection using Image Processing: A Survey  
  Authors : Anupama Pattanashetty; Dr. Suvarna Nandyal
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Image Processing is having is significance for disease detection on medical images. These disease recognition and classification approaches are specific to human organ and image type. One of such disease class includes detection of retinal disease such as glaucoma detection or diabetic detection. This paper shows an audit of most recent work on the utilization of image processing techniques for DR highlight identification. This present paper deals with the exhaustive review of literature based on different algorithms for the detection of diabetic retinopathy.


Published In : IJCSN Journal Volume 5, Issue 4

Date of Publication : August 2016

Pages : 661-666

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Anupama Pattanashetty : is currently a Research Scholar in Computer Science and Engineering Department at PDACE Kalaburgi. She has completed B.E from TCE Gadag and M.Tech from VTU RC Kalaburgi.

Dr Suvarna S Nandyal : born in Gulbarga, Karnataka, India in 1972. She received the B.E in Computer Science & engineering from Gulbarga University in 1993. M.Tech (Computer Science & Engineering) from VTU Belgaum in 2006. She has published number of papers in international journals and conferences. Her research interests include Image processing, Machine learning, Design & development of Mobile based Application, Computer Network, Multimedia Communication.








Glaucoma, Fundus Image, Diabetic Retinopathy, Hemorrhages, Blood Vessels, Exudes, Microaneurysms

This research survey paper depicts many works related to automated diabetic retinopathy (DR) detection, retinal veins are harmed because of liquid spillage from these vessels. Diverse injuries, i.e., Exudes, hemorrhages, microaneurysms, and textures are utilized to recognize the phase of DR. It is found that early determination of DR can lessen the possibility of vision misfortune up to half, Through the extensive literature review carried out it has been observed that though various methods for detection of DR have been carried out there is still a need and scope to develop a Computer Aided System which can not only help diagnose DR but would also help in checking the progression of the disease so that its growth can be restricted if not prevented. Lot of recent research is being carried for detection of DR and Glaucoma using fundus images, but still detection of progression of DR in patient remains to be researched. In future, we need to develop Hybrid techniques for more accurate, robust as well as affordable automated techniques for DR detection at low cast. Once DR is correctly diagnosed then they can take proper medicine or undergo surgery in a timely manner to avoid total blindness.


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