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@article{161577,
        author = {SR Ajitha and Dr G V Ramesh Babu},
        title = {Diabetic Retinopathy Classification using Various Machine Learning Techniques: A Review},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {5},
        pages = {64-69},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=161577},
        abstract = {Diabetes is common among those who have insulin issues. Only diabetics have diabetic retinopathy (DR), a devastating microvascular complication that destroys the retina and, if left misdiagnosed and untreated, can result in irreparable partial or whole blindness. In addition to the time it takes a patient to see an ophthalmologist who scans the patient's retina, many diabetics fail to recognize their illness and subsequently acquire visual impairments. Such human examination is time-consuming, slows the DR diagnosis method, allowing the illness to grow to more advanced stages within the window period, and is not always accurate. This article analyzes and evaluates the most recent research and survey articles addressing the precise diagnosis and classification of DR into distinct parts ranging from mild to severe.},
        keywords = {Diabetic Retinopathy, Convolutional Neural Networks, Fundus images},
        month = {},
        }