Generating synthetic MRI scans for improving Alzheimer's disease diagnosis

Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the leading cause of dementia. Magnetic Resonance Imaging (MRI) combined with Machine Learning (ML) enables early diagnosis, but ML models often underperform when trained on small, heterogeneous medical datasets. Transfer Learning (TL) helps mitigate this limitation, yet models pre-trained on 2D natural images still fall short of those trained directly on related 3D MRI data. To address this gap, we introduce an intermediate strategy based on synthetic data generation. Specifically, we propose a conditional Denoising Diffusion Probabilistic Model (DDPM) to synthesise 2D projections (axial, coronal, sagittal) of brain MRI scans across three clinical groups: Cognitively Normal (CN), Mild Cognitive Impairment (MCI), and AD. A total of 9000 synthetic images are used for pre-training 2D models, which are subsequently extended to 3D via axial, coronal, and sagittal convolutions and fine-tuned on real-world small datasets. Our method achieves 91.3% accuracy in binary (CN vs. AD) and 74.5% in three-class (CN/MCI/AD) classification on the 3T ADNI dataset, outperforming both models trained from scratch and those pre-trained on ImageNet. Our 2D ADnet achieved state-of-the-art performance on OASIS-2 (59.3% accuracy, 57.6% F1), surpassing all competitor models and confirming the robustness of synthetic data pre-training. These results show synthetic diffusion-based pre-training as a promising bridge between natural image TL and medical MRI data.