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@article{173900,
        author = {B. S. PANDA and K.NEHA SRI and CH.VARUN and CH.NITHIN SRIRAM and K. MANOJ KUMAR and K.DINESH},
        title = {VISIONCLEAR: using Deep Learning Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {1886-1890},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=173900},
        abstract = {Cataracts are a leading cause of visual impairment and blindness globally, particularly affecting the aging population. Timely detection is crucial for effective treatment and prevention of progression. This project presents an innovative approach to cataract detection using deep learning algorithms, specifically convolutional neural networks (s). The goal is to develop an automated system that accurately identifies the presence and severity of cataracts from ocular images. We compiled a comprehensive dataset containing thousands of retinal images, encompassing various stages of cataract development, sourced from diverse demographics.
The model was evaluated using a separate validation set, and performance metrics—including accuracy, sensitivity, specificity, and F1-score—were calculated. The results demonstrated that the Mobile Net achieved high accuracy in distinguishing between cataract-affected and healthy eyes, outperforming traditional image processing methods and existing machine learning models.},
        keywords = {Cataract detection, deep learning, convolutional neural networks, automated diagnosis, image processing, ocular images, machine learning, healthcare technology, visual impairment, early intervention},
        month = {March},
        }