Deep Learning–Driven Automated Grading of Diabetic Retinopathy Severity  Using Transfer Learning and Web-Based Deployment

Anil Khatak,Anju Gupta

Deep Learning–Driven Automated Grading of Diabetic Retinopathy Severity Using Transfer Learning and Web-Based Deployment

Authors

Anil Khatak,Anju Gupta

Keywords:

Diabetic Retinopathy, Deep Learning, Transfer Learning, ResNet50, Fundus Image Classification, Automated Screening, Healthcare AI Deployment

Abstract

As one of the most prevalent microvascular complications of diabetes, diabetic retinopathy (DR)remains a leading cause of vision loss that is largely preventable.” Early detection of DR is criticalto prevent permanent loss of vision”; however, traditional screening methods are labour-intensive,subjective, and heavily dependent

References

International Diabetes Federation (IDF), IDF Diabetes Atlas, 10th ed.

Wilkinson, C.P., et al., (2003). Proposed international clinical diabetic retinopathy and diabetic

macular edema disease severity scales. Ophthalmology, 110(9), 1677-1682

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Published

2024-10-15

How to Cite

Anil Khatak,Anju Gupta. (2024). Deep Learning–Driven Automated Grading of Diabetic Retinopathy Severity Using Transfer Learning and Web-Based Deployment . Journal of Computational Analysis and Applications (JoCAAA), 33(08), 6341–6355. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/3736