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@article{173392,
        author = {Pritesh Patil and Sushant Salunke and Pratik Murdare and Omkar Ghodke},
        title = {Diabetic Retinopathy Detection System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {592-595},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=173392},
        abstract = {Diabetic Retinopathy (DR) is a diabetes-related eye illness that can damage the retina and cause severe visual impairment. As a result, early detection of diabetic retinopathy is crucial for preventing blindness in humans. Our goal is to detect diabetic retinopathy using machine learning methods. As a result, we attempt to outline the numerous models and strategies used, as well as the methodologies employed, and examine the accuracies and outcomes. 
It will determine which method is most appropriate and accurate for prediction. To detect retinopathy in photos, machine learning goes through several processes, including converting the image to a suitable input format and applying various preprocessing algorithms. It also entails training a model on a training set and validating it with a different testing set. Methods offered in this study include image preprocessing, supervised learning, and feature extraction. First, the photos are pre-processed. They are converted. The image is also properly resized. Because the photos are heterogeneous, they are compressed into an appropriate size and format. The primary goal of this effort is to develop a reliable and noise-tolerant system for detecting diabetic retinopathy.},
        keywords = {Machine learning, Diabetic Retinopathy.},
        month = {March},
        }