Retina Segmentation using UNET and Diabetic Retinopathy Detection

Author(s)	Mr. D.Bikshalu, M.Vaishanvi, P.Rajsri, K.UshaRani, V.Keerthana
Country	India
Abstract	Diabetic Retinopathy (DR) is a leading cause of vision impairment and blindness among individuals with diabetes, especially when not diagnosed and treated early. Timely detection of retinal abnormalities is crucial for effective management and treatment of DR. In this project, we propose an automated system for retinal image analysis that combines U-Net-based retina segmentation with Convolution Neural Network (CNN)-based DR classification [1]. The system is designed to first segment critical structures in retinal images, such as blood vessels, the optic disc, and the macula, using a U-Net architecture, which excels at biomedical image segmentation due to its encoder-decoder structure with skip connections. The system shows promise for use in clinical settings and telemedicine platforms, enabling early and reliable DR screening, especially in under-resourced areas.
Field	Engineering
Published In	Volume 17, Issue 1, January-June 2026
Published On	2026-04-12
Cite This	Retina Segmentation using UNET and Diabetic Retinopathy Detection - Mr. D.Bikshalu, M.Vaishanvi, P.Rajsri, K.UshaRani, V.Keerthana - IJAIDR Volume 17, Issue 1, January-June 2026.

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