Ocular Disease Classification Using CNN with Deep Convolutional Generative Adversarial Network

TL;DR

This paper introduces a GAN-based data augmentation method to improve CNN classification of ocular diseases, achieving high accuracy despite limited original datasets.

Contribution

It presents a novel approach combining GANs with CNNs to enhance ocular disease classification accuracy using synthetic data.

Findings

Achieved 78.6% accuracy for myopia

Achieved 88.6% accuracy for glaucoma

Achieved 84.6% overall accuracy

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%.