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@article{178695,
        author = {Ms K.Sudharani and A.Akanksha and CH.Lahari and D.Devender},
        title = {DIABETIC RETINOPATHY PREDICTION USING MACHINE LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {5053-5057},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178695},
        abstract = {Diabetic Retinopathy (DR) is a severe complication of diabetes that can lead to irreversible vision loss if not detected and treated in a timely manner. With the global prevalence of diabetes on the rise, efficient and accurate screening for DR has become crucial. Traditional diagnostic methods rely heavily on manual inspection of retinal images by ophthalmologists, a process that is time-consuming, prone to human error, and limited by the availability of trained specialists. This project addresses these challenges by developing a machine learning-based predictive model for automated DR detection using a large dataset of retina images. Advanced deep learning techniques, including Convolutional Neural Networks (CNN), DenseNet, and MobileNet, are employed to extract intricate patterns and features from retinal images, enhancing diagnostic accuracy. The proposed system aims to enable faster, consistent, and more reliable detection of DR at early stages, ultimately improving patient outcomes and reducing the risk of vision loss. This project demonstrates how integrating artificial intelligence into ophthalmology can significantly advance diabetic eye cares},
        keywords = {Diabetic Retinopathy, Machine Learning,Deep Learning,Convolutional Neural Networks (CNN),DenseNet,Retina Image Analysis, Automated Diagnosis Ophthalmology, Diabetes Complications.},
        month = {May},
        }