Abstract:
Diabetic Retinopathy (DR) is a diabetes complication
that causes damage to the blood vessels of the light sensitive
tissue at the back of the eye. All the people who are suffering from
diabetes have a high risk of subjecting to DR which may lead to
total blindness. Red lesions, cotton-wool spots and exudates are
symptoms of non proliferative diabetic retinopathy which is the
early stage of diabetic retinopathy. When the disease develops
to proliferative diabetic retinopathy fluid leaking from retinal
capillaries and the formation of new vessels on the surface of
the retina happens. At this stage there is a very low possibility
of preventing total blindness. Therefore, early detection of DR is
important to prevent vision loss. So, if there is an easy way of
detecting early signs of DR accurately that will be beneficial. Red
lesion detection is more important for the early identification of
DR. In this paper, we are proposing a method for the automated
detection of red lesions in retinal images using image processing
techniques and machine learning. The developed algorithm has
sensitivity and specificity of 92.05% and 88.68% respectively.
