2024
DOI: 10.3390/electronics13183671
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Comprehensive Data Augmentation Approach Using WGAN-GP and UMAP for Enhancing Alzheimer’s Disease Diagnosis

Emi Yuda,
Tomoki Ando,
Itaru Kaneko
et al.

Abstract: In this study, the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) was used to improve the diagnosis of Alzheimer’s disease using medical imaging and the Alzheimer’s disease image dataset across four diagnostic classes. The WGAN-GP was employed for data augmentation. The original dataset, the augmented dataset and the combined data were mapped using Uniform Manifold Approximation and Projection (UMAP) in both a 2D and 3D space. The same combined interaction network analysis was then … Show more

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