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@article{178615,
        author = {Smt.KAVYA SN and Ms.BHAVANI NB and Ms.BHUVANA S and Ms.DEEKSHITHA V and Ms.SUSHMA KU},
        title = {DEEP LEARNING FOR CARDIOVASCULAR RISK DETECTION FROM RETINAL EYE IMAGE},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {4127-4137},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178615},
        abstract = {Cardiovascular diseases (CVDs) remain a leading cause of global morbidity and mortality. Early detection and intervention are crucial for improving patient outcomes and reducing the burden on healthcare systems. Recent research suggests a potential link between retinal vascular changes and cardiovascular health. Retinal images offer a non-invasive means to assess microvascular abnormalities, making them an attractive source of data for predictive modeling. This project focuses on developing a machine learning model, specifically using Recurrent Neural Networks (RNNs), to analyze retinal images and detect patterns indicative of heart diseases. RNNs are well-suited for processing sequential data, making them suitable for capturing temporal dependencies in the retinal images and improving the predictive accuracy of the model.},
        keywords = {},
        month = {May},
        }