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@article{186826,
        author = {Sandehep.S.S and Riswant.R.G and Sanjay.M and Dr.B.Vanathi},
        title = {DIABETIC RETINOPATHY PREDICTION USING XCEPTION (DEEP LEARNING APPROACH)},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {1944-1949},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=186826},
        abstract = {Prolonged high blood sugar levels induce diabetic retinopathy (DR), a serious eye disorder that damages the retina's blood vessels and, if ignored, can result in blindness or visual loss. Preventing visual impairment requires early detection and action. This study aims to develop a deep learning-based system that automatically detects and classifies DR from retinal fundus images using the Xception architecture. The model provides a quick, inexpensive, and non-invasive screening method by learning to recognize and evaluate the severity of DR by training on a dataset of annotated retinal pictures. This technique may help ophthalmologists diagnose DR early, which would enable prompt treatment and efficient disease management. The experiment shows how deep learning can improve ophthalmology diagnosis speed and accuracy, especially in impoverished regions where specialized care may not be readily available. This project's automated detection model has great promise as a quick, affordable screening tool that can help with early diagnosis and enhance patient outcomes. In the end, this experiment highlights how deep learning is revolutionizing ocular diagnostics and improving diabetic patients' preventive healthcare.},
        keywords = {Automated Detection, Diabetic Retinopathy, Early Diagnosis, Deep Learning, Retinal Fundus Images, Xception Architecture.},
        month = {November},
        }