Eye Disease Prediction System

Abstract

"This study aims to create an advanced eye disease prediction system capable of anticipating the onset of three common eye conditions: cataract, uveitis, and conjunctivitis. Employing state-of-the-art machine learning techniques, the system will be trained on a diverse dataset of eye scans from patients with various ailments. This innovation promises to streamline and enhance the diagnostic process, without posing any risks.

The proposed system will be founded on a Convolutional Neural Network (CNN) architecture comprising 27 layers. Through extensive training on a large repository of eye scans, it will provide accurate assessments of eye disease presence. The model's achieved accuracy stands at an impressive 77%, affirming its reliability and credibility.

The advent of this precise and dependable eye disease prediction system holds significant implications for the medical field. Elevating the standard of eye care and minimizing the likelihood of misdiagnoses, this system empowers doctors to deliver swifter and more accurate diagnoses, ultimately leading to improved patient outcomes. Moreover, the study's insights will serve as a foundation for creating trustworthy diagnostic tools with broad applications across various medical disciplines."