Authors : Govind Haldankar; Gaurav Galbal; Vikram Choudhary; Sanket Kanoja

Volume/Issue : Volume 9 - 2024, Issue 7 - July

Google Scholar : https://tinyurl.com/y7e37xva

Scribd : https://tinyurl.com/mt7ry5wb

DOI : https://doi.org/10.38124/ijisrt/IJISRT24JUL605

Abstract : Diabetic retinopathy (DR) is the major cause of vision impairment and blindness in diabetics. Early detection and treatments are critical in preventing irreparable retinal damage. Manual detection of diabetic retinopathy by an ophthalmologist takes a long time, and patients must suffer greatly during this time. This paper presents an automated approach for rapid DR detection using the DenseNet-121 architecture. Our model achieves an accuracy exceeding 80%, with a precision score of 81% and a recall score of 86%, indicating its high effectiveness in detecting DR. Additionally, we developed a server-based implementation where the trained model is deployed. Images captured by a camera are uploaded to a cloud server, which processes them and sends back a diagnostic response. This study contributes to continuing efforts to create efficient and reliable techniques for early DR identification, resulting in earliermanagement and better patient outcomes.

Keywords : Diabetic Retinopathy, DenseNet Architecture, Retinal Fundus Images, Cloud Server, Real-time Screening.

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