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@article{166277,
        author = {B Saratha and Dr. V.Shenbaga Priya},
        title = {Enhancing Early Detection of Diabetic Retinopathy using Machine Learning Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {2},
        pages = {379-384},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=166277},
        abstract = {The advancement of technology in the medical field has led to the development of powerful methods for the early detection of various illnesses. Diabetic retinopathy, a serious health condition caused by long-term diabetes, affects the retina and can lead to severe visual impairment. Early diagnosis is crucial for effective management and treatment. This study explores various techniques for the early detection of diabetic retinopathy using clinical data and screening images, such as pathology reports and optical coherence tomography (OCT) fundus images. The research focuses on the application of machine learning and deep learning algorithms, specifically boosting algorithms, to improve the accuracy of early diagnosis. Statistical analysis tools such as mean square error (MSE), mean absolute error (MAE), and Huber Loss (HL) are utilized to evaluate the performance of these algorithms. The findings demonstrate that a combination of indicators enhances the identification of diabetic retinopathy, providing a robust framework for its early detection.},
        keywords = {Boosting Algorithms, Diabetic retinopathy, loss function, machine learning, neural networks.},
        month = {July},
        }