Leveraging Diffusion-Based Models for Data Augmentation in Maternal-Fetal Ultrasound Classification

This paper investigates diffusion models for data augmentation in fetal ultrasound image classification, addressing the critical challenge of limited medical image datasets for rare conditions. We fine-tuned Stable Diffusion with Low-Rank Adaptation (LoRA) to generate synthetic ultrasound images and systematically evaluated their impact on classification performance across multiple experimental conditions. Our results demonstrate that augmenting training data with synthetic images significantly improves classification accuracy and effectively addresses class imbalance issues. Notably, models trained with a combination of real and synthetic data showed enhanced generalization capabilities and improved performance on minority classes. These findings establish diffusion-based augmentation as a promising approach to overcome data scarcity constraints in medical imaging applications, with potential implications for clinical decision support systems.