Comparative Analysis of Diffusion Models for Enhancing Alzheimer's Disease Classification

Abstract

Early and accurate detection of Alzheimers disease (AD) is vital for timely intervention and better patient outcomes. However, training machine learning (ML) models for this purpose is challenging due to the limited medical images available and the imbalance of classes. The size and quality of the training dataset directly affect model performance. Recent advances in diffusion models address this limitation by generating synthetic images from a small sample of real images. In this work, we adapted two diffusion models and trained VGG16 and ConvNeXt classification models for AD classification. The first diffusion model was a Denoising Diffusion Probabilistic Model (DDPM) with a U-Net architecture, and the second was a U-KAN framework that integrates Kolmogorov-Arnold Networks (KANs) with the U-Net. Both models were fine-tuned to generate MRI scans of AD or Late Mild Cognitive Impairment (LMCI). We conducted a comparative analysis to assess the reliability and usefulness of these synthetic images for training classification models. The best metrics achieved by the classification models using synthetic images for the AD class were precision of 96%, recall of 83%, F1 score of 87%, and AUC of 0.88. For the LMCI class, the best values were precision of 78%, recall of 88%, F1 score of 82%, and AUC of 0.88. They both demonstrated noticeable improvement from the baseline trained only on the original images.