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.
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.