Advanced International Journal for Research

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Volume 7 Issue 5
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Deep Convolutional Neural Networks For Diabetic Retinopathy And Glaucoma Screening From Retinal Fundus Images: A Literature Review With Emphasis On Rural India

Author(s) Ms. Sanika Ratanrao Kolhe, Dr. Abhijit Narayanrao Banubakode
Country India
Abstract Diabetic retinopathy (DR) and glaucoma are major causes of preventable blindness, and both can be picked up on a colour fundus photograph before the patient notices any symptom. In rural India, though, ophthalmologists are few compared with the number of people at risk, so many patients are seen late or not at all. Convolutional neural networks (CNN) have been reported to grade fundus images about as well as trained graders, and a number of studies have tested them in Indian hospitals and community camps. This paper reviews that literature from a computer engineering perspective. It covers the clinical background, public datasets and preprocessing, the development of CNN architectures, published DR and glaucoma detection studies, and the practical problems of deploying such systems with smartphone-based cameras and poor connectivity. The review finds that DR detection is considerably more mature than glaucoma detection, that most of the evidence comes from well-resourced centres, and that image quality, dataset shift, explainability, joint multi-disease screening and regulatory readiness are still open problems. A framework for a rural screening pipeline and an evaluation plan are outlined to guide future work.
Keywords Diabetic Retinopathy, Glaucoma, Retinal Fundus Images, Convolutional Neural Networks, Deep Learning, Medical Image Analysis, Rural Healthcare, India.
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 7, Issue 5, September-October 2026
Published On 2026-10-20
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E-ISSN 3048-7641
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