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@article{170595,
        author = {Geeta R. Kulkarni and Pallavi S. Bijjargi and Dhanashri R. Sutar and Manisha D. Mudagi and Varsha S. Koli and Paurnima U. Shinde},
        title = {AI-Based Dry Eye Disease Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {7},
        pages = {1027-1029},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=170595},
        abstract = {AI-Based Dry Eye Disease Detecction is advanced AI techniques for detecting and predicting dry eye disease (DED) using a multi-faceted approach. It employs a Multi-Layer Perceptron (MLP) to analyzes patient-reported symptoms, capturing complex patterns for disease prediction. MobileNet, a lightweight neural network, processes visual data to classify eye diseases efficiently, ideal for real-time and resource-limited environments. VGG-19, another deep learning model, analyze blink patterns to assess blink frequency, offering insights into DED severity. By integrating these models, the system enhances diagnostic accuracy, improves early detection, and supports better management of dry eye disease.},
        keywords = {},
        month = {December},
        }