Enhancing Eye Disease Diagnosis with Deep Learning and Synthetic Data Augmentation

TL;DR

This paper proposes an ensemble deep learning model combining CNNs and GANs to improve early diabetic retinopathy detection, achieving 99% validation accuracy on the APTOS dataset.

Contribution

It introduces a hybrid ensemble approach that enhances CNN performance for DR diagnosis, surpassing previous models in accuracy.

Findings

Achieved 99% validation accuracy on APTOS dataset

Demonstrated superiority over existing models

Improved early detection of diabetic retinopathy

Abstract

In recent years, the focus is on improving the diagnosis of diabetic retinopathy (DR) using machine learning and deep learning technologies. Researchers have explored various approaches, including the use of high-definition medical imaging, AI-driven algorithms such as convolutional neural networks (CNNs) and generative adversarial networks (GANs). Among all the available tools, CNNs have emerged as a preferred tool due to their superior classification accuracy and efficiency. Although the accuracy of CNNs is comparatively better but it can be improved by introducing some hybrid models by combining various machine learning and deep learning models. Therefore, in this paper, an ensemble learning technique is proposed for early detection and management of DR with higher accuracy. The proposed model is tested on the APTOS dataset and it is showing supremacy on the validation accuracy ($99\%)$ in comparison to the previous models. Hence, the model can be helpful for early detection and treatment of the DR, thereby enhancing the overall quality of care for affected individuals.