Comprehensive Data Augmentation Approach Using WGAN-GP and UMAP for Enhancing Alzheimer’s Disease Diagnosis

IF 2.6 3区 工程技术 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Emi Yuda, Tomoki Ando, Itaru Kaneko, Yutaka Yoshida, Daisuke Hirahara
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引用次数: 0

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 performed on the test data. The results showed that, for the test accuracy, the score was 30.46% for the original dataset (unbalanced), whereas for the WGAN-GP augmented dataset (balanced), it improved to 56.84%, indicating that the WGAN-GP augmentation can effectively address the unbalanced problem.
利用 WGAN-GP 和 UMAP 增强阿尔茨海默病诊断的综合数据增强方法
本研究利用具有梯度惩罚的瓦瑟斯坦生成对抗网络(WGAN-GP)改进了利用医学影像和阿尔茨海默病图像数据集对阿尔茨海默病进行的四类诊断。WGAN-GP 被用于数据扩增。原始数据集、扩增数据集和组合数据在二维和三维空间中都使用了统一曲面逼近和投影(UMAP)技术进行了映射。然后对测试数据进行了同样的组合交互网络分析。结果显示,原始数据集(不平衡)的测试准确率为 30.46%,而 WGAN-GP 扩增数据集(平衡)的测试准确率提高到 56.84%,这表明 WGAN-GP 扩增能有效解决不平衡问题。
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来源期刊
Electronics
Electronics Computer Science-Computer Networks and Communications
CiteScore
1.10
自引率
10.30%
发文量
3515
审稿时长
16.71 days
期刊介绍: Electronics (ISSN 2079-9292; CODEN: ELECGJ) is an international, open access journal on the science of electronics and its applications published quarterly online by MDPI.
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