Ocular Disease Classification Using CNN with Deep Convolutional Generative Adversarial Network
Journal:
arXiv
Published Date:
Feb 14, 2025
Abstract
The Convolutional Neural Network (CNN) has shown impressive performance in
image classification because of its strong learning capabilities. However, it
demands a substantial and balanced dataset for effective training. Otherwise,
networks frequently exhibit over fitting and struggle to generalize to new
examples. Publicly available dataset of fundus images of ocular disease is
insufficient to train any classification model to achieve satisfactory
accuracy. So, we propose Generative Adversarial Network(GAN) based data
generation technique to synthesize dataset for training CNN based
classification model and later use original disease containing ocular images to
test the model. During testing the model classification accuracy with the
original ocular image, the model achieves an accuracy rate of 78.6% for myopia,
88.6% for glaucoma, and 84.6% for cataract, with an overall classification
accuracy of 84.6%.
