用GAN生成脑部MRI数据，提升阿尔茨海默病预测准确率
Enhancing Alzheimer's Disease Prediction: A Novel Approach to Leveraging GAN-Augmented Data for Improved CNN Model Accuracy
- 用GAN生成高质量脑部MRI图像，扩充小样本数据集
- 结合SSMI指标筛选优质合成数据，模型准确率达92.3%
- 适合医学影像分析与小样本学习研究者参考
阿尔茨海默病（AD）是全球影响数百万人的神经退行性疾病。随着发病率持续上升，早期诊断对改善临床预后至关重要。卷积神经网络（CNN）在诊断阿尔茨海默病方面展现出潜力，但当数据量较少时，其验证准确率往往偏低。为此，本文利用生成对抗网络（GAN）生成合成脑部MRI数据以扩充现有数据集，从而提升模型性能。研究中创新性地引入SSMI指标，用于筛选由GAN生成的高质量合成数据，并与随机打乱的合成数据进行对比。实验结果表明，采用含SSMI筛选的合成数据后，模型验证准确率达到92.3%，显著高于传统数据集表现。
原文摘要 · Abstract (English)
Alzheimer's Disease (AD) is a neurodegenerative disease affecting millions of individuals across the globe. As the prevalence of this disease continues to rise, early diagnosis is crucial to improve clinical outcomes. Neural networks, specifically Convolutional Neural Networks (CNNs), are promising tools for diagnosing individuals with Alzheimer's. However, neural networks such as ANNs and CNNs typically yield lower validation accuracies when fed lower quantities of data. Hence, Generative Adversarial Networks (GANs) can be utilized to synthesize data to augment these existing MRI datasets, potentially yielding higher validation accuracies. In this study, we use this principle while examining a novel application of the SSMI metric in selecting high-quality synthetic data generated by our GAN to compare its accuracies with shuffled data generated by our GAN. We observed that incorporating GANs with an SSMI metric returned the highest accuracies when compared to a traditional dataset.
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