Employment of Convolutional Neural Networks in an Eye Disease Detection Application Leveraging Tensorflow.js
(1) University of Ibrahimy
(2) University of Ibrahimy
(3) University of Ibrahimy
Abstract
according to data from the World Health Organization. In Indonesia, these
conditions rank first in Southeast Asia and second globally, as evidenced by
data from the Ministry of Health's Roadmap of Visual Impairment Control
Program in Indonesia 2017-2030. Early detection of these diseases is crucial
for preventing blindness. This study aims to classify eye diseases using a native-
architecture Convolutional Neural Network (CNN) classification method with
the novel inclusion of three non-fundus or real-eye image subsets. The CNN
implementation in this study employs 100 epochs and achieves an accuracy of
98.67%. The saved model from this research will be deployed using
TensorFlow.js, a framework or library derived from TensorFlow.
Keywords
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