Enhancing Alzheimer's Disease Prediction: A Novel Approach to Leveraging GAN-Augmented Data for Improved CNN Model Accuracy

TL;DR

This paper proposes a novel GAN-based data augmentation method using the SSMI metric to improve CNN accuracy in Alzheimer's disease diagnosis from MRI data.

Contribution

It introduces a new approach combining GANs and the SSMI metric for selecting high-quality synthetic data to enhance CNN model performance.

Findings

GAN-augmented data with SSMI yields higher validation accuracy

SSMI metric effectively selects high-quality synthetic MRI data

Improved CNN diagnostic accuracy for Alzheimer's disease

Abstract

Alzheimer's Disease (AD) is a neurodegenerative disease affecting millions of individuals across the globe. As the prevalence of this disease continues to rise, early diagnosis is crucial to improve clinical outcomes. Neural networks, specifically Convolutional Neural Networks (CNNs), are promising tools for diagnosing individuals with Alzheimer's. However, neural networks such as ANNs and CNNs typically yield lower validation accuracies when fed lower quantities of data. Hence, Generative Adversarial Networks (GANs) can be utilized to synthesize data to augment these existing MRI datasets, potentially yielding higher validation accuracies. In this study, we use this principle while examining a novel application of the SSMI metric in selecting high-quality synthetic data generated by our GAN to compare its accuracies with shuffled data generated by our GAN. We observed that incorporating GANs with an SSMI metric returned the highest accuracies when compared to a traditional dataset.