Authors :
Satish Kumar Kushwaha; Dr. Neelesh Jain; Shekhar Nigam
Volume/Issue :
Volume 8 - 2023, Issue 7 - July
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://tinyurl.com/ye8t2n2y
DOI :
https://doi.org/10.5281/zenodo.8223773
Abstract :
When a person has diabetic retinopathy (DR),
even after having the condition for a long time, they are
highly unlikely to be aware of it. Not everyone is really
familiar with this illness. This illness is a little different
from others since, depending on the diagnostic
syndrome, every diabetes patient has a risk of
developing diabetic retinopathy. Various studies have
been done in this area, but a good method is still
needed. A neural network in machine learning needs to
be trained very well because if it isn't, the system won't
be able to provide decent results. The rate of false
alarms is higher due to poor training. However, there is
another method—an edge detection tool—by which DR
may be detected more accurately. Edge hasthe ability to
extract the geometry of impairments, and the density of
the retrieved region determines whether or not it is
diabetic retinopathy. The exudates from the fundus
picture are extracted by the proposed method using the
Sobel Edge Detection tool. Prior to that approach, a
colour mapping tool was used to make exudates from the
fundus picture more visible. A colour mapping tool helps
improve the visibility of some patches that the illness
may cause. The backdrop can also be classified by
changing the colours such that exudates are more
obvious than in the original image. The suggested system
has more accuracy than the existing model and is tested
using the Messidor benchmark.
Keywords :
Diabetic Retinopathy, Fundus Image, Sobel EdgeDetection, Color Mapping, Retina, Optic Cup.
When a person has diabetic retinopathy (DR),
even after having the condition for a long time, they are
highly unlikely to be aware of it. Not everyone is really
familiar with this illness. This illness is a little different
from others since, depending on the diagnostic
syndrome, every diabetes patient has a risk of
developing diabetic retinopathy. Various studies have
been done in this area, but a good method is still
needed. A neural network in machine learning needs to
be trained very well because if it isn't, the system won't
be able to provide decent results. The rate of false
alarms is higher due to poor training. However, there is
another method—an edge detection tool—by which DR
may be detected more accurately. Edge hasthe ability to
extract the geometry of impairments, and the density of
the retrieved region determines whether or not it is
diabetic retinopathy. The exudates from the fundus
picture are extracted by the proposed method using the
Sobel Edge Detection tool. Prior to that approach, a
colour mapping tool was used to make exudates from the
fundus picture more visible. A colour mapping tool helps
improve the visibility of some patches that the illness
may cause. The backdrop can also be classified by
changing the colours such that exudates are more
obvious than in the original image. The suggested system
has more accuracy than the existing model and is tested
using the Messidor benchmark.
Keywords :
Diabetic Retinopathy, Fundus Image, Sobel EdgeDetection, Color Mapping, Retina, Optic Cup.
