JJEM: Special Edition 2 (December 2024)
JJEM: Special Edition 2 (December 2024)
2024-12-08
Efficient and Scalable CNN model of Diabetic Retinopathy Detection.
Priya Hegde, Mr. Santhosh S G
Metabolic disorder issues, particularly diabetic retinal disease (DR), affect the retina of the eye and are classified into five severity levels: normal, mild, medium, severe, and proliferative. If not discovered and treated, this illness might result in blindness. This issue is still identified and classified manually by an ophthalmologist using a photograph of the patient's eye fundus. Manual detection has the disadvantage of requiring sector experience and making the task more challenging. Convolutional neural networks CNN was used in this search for a category of DR illnesses. A CNN Method-driven on the VGG-16 architecture has been Established to improve ocular fundus pictures of DR patients. The planned technique is implemented in several stages, including data Gathering, Data refinement, augmentation, and modelling.
Diabetic Retinopathy (DR), Convolutional Neural Network (CNN), Visual Geometry Group-16 Architecture, Retinal Colour Fundus Photos, Blindness.