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@article{193734,
        author = {P. Sudheer and C.Pavani and Bejjiparapu Sravanthi and Ummadi Ramya Sree and Ampabathina Sathish Kumar and Tirupathi Muni Teja},
        title = {Cataract Diseases In Retinal Images Using Vision Transformer Architecture},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {2365-2372},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=193734},
        abstract = {The eye is one of the most important sensory organs in humans, and diseases affecting the retina can significantly impact vision and quality of life. Cataract is a common eye disorder that causes clouding of the eye lens and may lead to vision impairment if not detected early. With the rapid advancement of medical imaging and artificial intelligence, automated techniques have become essential for assisting in the early diagnosis of eye diseases. This project focuses on the classification of cataract disease using retinal images through a deep learning-based approach. A Vision Transformer (VIT) model is employed to analyze retinal images by leveraging self-attention mechanisms that effectively capture global contextual relationships between image regions. Unlike conventional Convolutional Neural Networks (CNNs), the Vision Transformer processes image patches directly, enabling improved modelling of long-range dependencies. Multiple experimental scenarios are explored by varying hyperparameters such as epochs, optimizers, learning rates, and input image dimensions to enhance the robustness of the system. The proposed approach aims to provide a fast, reliable, and efficient solution for automated cataract detection, supporting healthcare professionals in early diagnosis and clinical decision-making.},
        keywords = {Cataract Detection, Retinal Image Classification, Vision Transformer, Deep Learning, Medical Image Analysis.},
        month = {March},
        }