Comparative Evaluation of StyleGAN3-Based Augmentation Strategies for Enhanced Medical Image Classification

Abstract

Deep learning models for medical image classification face significant challenges due to class imbalance and the limited availability of annotated datasets, particularly for rare diseases. Traditional data augmentation techniques, such as rotation, translation, etc., often fail to provide sufficient diversity to perform a good classification for minor classes. To address this issue, various strategies have been explored, including oversampling, undersampling, cost-sensitive learning, and synthetic data generation using generative adversarial networks (GANs). In this study, we evaluate the impact of using a generative AI based approaches and demonstrate that the most effective strategy is to combine synthetic augmentation with traditional methods. Specifically, we employ StyleGAN3 to generate high-fidelity synthetic images that, when integrated with traditional data-augmentation techniques, may improve the performance of deep learning models on medical image classification. We validate our method on datasets, including COVID-19 chest X-rays and HAM10000. Experimental results show that this hybrid approach leads to an improvement in classification accuracy, particularly for minority classes, surpassing standalone augmentation strategies. Our findings highlight the potential of AI-driven synthetic data generation as a complementary solution to traditional augmentation, offering a more balanced and diverse dataset for medical image analysis.