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@article{191537,
        author = {Dr krishna Kumar P R and Saswati Behera and Dr Smitha Kurian and Mukthi K},
        title = {Attention-Based Deep Learning Models for Retinal Image Analysis in Diabetic Retinopathy},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {7056-7062},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=191537},
        abstract = {Diabetic retinopathy (DR) is an extreme microvascular complication of diabetes mellitus and a major cause of preventable blindness globally. Chronic hyperglycemia damages retinal blood vessels, causing leakage, ischemia, and massive neovascularization, ultimately resulting in vision loss or blindness. With the rising incidence of diabetes – particularly in developing international locations – the burden of DR is increasing unpredictably.
Early assessment reduces vision loss to a remarkable degree; However, traditional DR screening involves manual examination of retinal fundus images by ophthalmologists. This technique is labor-intensive, extremely expensive, and prone to inter-observer variability. In addition, many rural and underdeveloped regions lack adequate ophthalmologists, making large-scale screening challenging.},
        keywords = {},
        month = {January},
        }