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Automated Eye Diseases Recognition Web-Application Using Convolutional Neural Networks

Aahn Deshpande, Shubham Kumar, Kalash Butola, Harshit Pandey, Jyoti Gupta

Abstract


The World Health Organization (WHO) estimates that more than 2 billion people worldwide experience close-up or distance vision issues. This study presents a method for creating an API for a proposed Deep learning CNN’s sequential model that can instantly and efficiently determine if a user has eye diseases such as glaucoma, crossed eyes, uveitis, cataracts, and bulging eyes. The API allows for easy integration into various applications, providing a valuable tool for developers and researchers. The system allows users to upload their eye images for diagnosis, with an accuracy of 97%. It is intended to assist ophthalmologists, not replace them. The proposed model aims to address the global issue of vision problems as reported by the World Health Organization, and provide a solution to ease the workload of ophthalmologists while increasing the detection accuracy of eye-related diseases.


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References


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