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Detection and diabetic retinopathy grading using digital retinal images.

Malhi, A., Grewal, R. and Pannu, H. S., 2023. Detection and diabetic retinopathy grading using digital retinal images. International Journal of Intelligent Robotics and Applications. (In Press)

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s41315-022-00269-5.pdf - Published Version
Available under License Creative Commons Attribution.


DOI: 10.1007/s41315-022-00269-5


Diabetic Retinopathy is an eye disorder that affects people suffering from diabetes. Higher sugar levels in blood leads to damage of blood vessels in eyes and may even cause blindness. Diabetic retinopathy is identified by red spots known as microanuerysms and bright yellow lesions called exudates. It has been observed that early detection of exudates and microaneurysms may save the patient’s vision and this paper proposes a simple and effective technique for diabetic retinopathy. Both publicly available and real time datasets of colored images captured by fundus camera have been used for the empirical analysis. In the proposed work, grading has been done to know the severity of diabetic retinopathy i.e. whether it is mild, moderate or severe using exudates and micro aneurysms in the fundus images. An automated approach that uses image processing, features extraction and machine learning models to predict accurately the presence of the exudates and micro aneurysms which can be used for grading has been proposed. The research is carried out in two segments; one for exudates and another for micro aneurysms. The grading via exudates is done based upon their distance from macula whereas grading via micro aneurysms is done by calculating their count. For grading using exudates, support vector machine and K-Nearest neighbor show the highest accuracy of 92.1% and for grading using micro aneurysms, decision tree shows the highest accuracy of 99.9% in prediction of severity levels of the disease.

Item Type:Article
Uncontrolled Keywords:Machine learning; Image processing; Diabetic retinopathy; Exudates Microaneurysms
Group:Faculty of Science & Technology
ID Code:38125
Deposited By: Symplectic RT2
Deposited On:02 Feb 2023 13:00
Last Modified:02 Feb 2023 13:00


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