阿尔兹海默症，英文简称 AD，是一种在老年群体中常见的普遍性

神经退行性疾病。由于全球人口老龄化的趋势，患有阿尔茨海默症（AD）

的人数也在不断增加。受当前的医疗诊断条件限制，利用现有的医学

知识难以迅速且准确地诊断此类疾病。这导致许多患有阿尔兹海默症

的患者被误诊为正常的老化过程，而错失了最佳治疗干预时间。因此

及早诊断和干预 AD 患者对减缓其进展具有至关重要的意义。

目前，医学影像技术是探究人类大脑结构与功能的一种重要手段，

其中磁共振成像（MRI）能够提供大脑内部结构的高清晰度图像，对

于早期阿尔兹海默病的诊断与预后评估起着关键作用。本研究基于阿

尔兹海默症患者的 MRI 影像数据，利用深度学习技术构建了一种用于

AD 检测的诊断模型。同时针对深度学习模型训练过程中医学数据规

模不足的问题，本研究采用了深度卷积生成对抗网络（DCGAN）对医

学影像数据进行增强，旨在提升模型的泛化性能，以期实现对 AD 的

高精度诊断。

本文首先使用随机旋转和位移转换等数据增强方法对实验数据进

行了预处理和标准化。其次，本文部署了 VGG16 网络，并将其作为基

础架构。微调过程为在预训练的 VGG16 模型中添加批处理归一化（BN）

层，以优化训练过程并提高模型收敛的速度。批处理归一化层通过调

整网络中间层的输入分布来减少内部协变量的位移，使模型能够更好

地学习和泛化，最终达到 85.2%的检测精度。

通过对 DCGAN 进行训练以掌握真实 MRI 图像的分布特征，我们能

够创造出在视觉上几乎无法与实际核磁共振成像区别开来的新的高

品质的图像数据。这种数据集的扩充不仅增加了训练样本的多样性，

而且还进一步增强了模型的泛化性能。

综上所述，通过整合 VGG16 网络的深度特征提取能力与 DCGAN 生

成的高质量数据增强策略，本文构建的 AD 诊断模型展现出了显著的

性能提升。实验结果表明，该模型在 AD 早期诊断方面，相比于使用

原始数据集训练的深度学习模型，具有更高的准确率和灵敏度。这一

进步不仅证明了深度学习技术在医学影像分析领域的应用潜力，也为

阿尔兹海默症的早期检测与诊断提供了新的技术途径。

Alzheimer's Disease (AD) is a common neurodegenerative disorder prevalent among the elderly population. With the global trend towards an aging population, the number of AD cases continues to rise. Due to the limitations of current medical diagnostic capabilities and the complexity of utilizing existing medical knowledge for rapid and accurate diagnosis, many patients suffering from Alzheimer's Disease are misdiagnosed as experiencing normal aging processes, missing the critical window for optimal therapeutic intervention. Therefore, early diagnosis and intervention for patients with AD are crucial for slowing the progression of the disease.

Medical imaging technology plays a vital role in exploring the structure and function of the human brain. Magnetic Resonance Imaging (MRI) provides high-resolution images of the brain's internal structure, playing a key role in the early diagnosis and prognostic assessment of Alzheimer's Disease. This study builds a diagnostic model for AD detection using MRI data from patients with Alzheimer's Disease, employing deep learning technology. To address the issue of insufficient medical data during the training process of deep learning models, this research utilizes Deep Convolutional Generative Adversarial Networks (DCGAN) to enhance the medical imaging data, aiming to improve the generalization performance of the model for high-precision AD diagnosis.

The manuscript initiates by subjecting the experimental data to preprocessing, incorporating data augmentation techniques including random rotations and cropping transformations, succeeded by the application of normalization. Subsequently, the VGG16 network is adopted as the base architecture and fine-tuned. The fine-tuning process includes the addition of Batch Normalization (BN) layers on top of the pre-trained VGG16 model to optimize the training process and enhance model convergence. The batch normalization layers adjust the distribution of inputs in the network's intermediate layers, reducing internal covariate shift, thereby enabling better learning and generalization of the model, ultimately achieving an identification accuracy of 85%.

By training DCGAN to learn the distribution of real MRI images, this study is able to generate new, high-quality imaging data that are visually indistinguishable from real MRI images. These generated images are used to expand the original dataset, providing a richer set of training samples and further improving the model's identification capabilities.

In summary, by integrating the deep feature extraction capabilities of the VGG16 network with the high-quality data enhancement strategy of DCGAN, the AD diagnostic model developed in this paper demonstrates significant performance improvements. Experimental results show that, in the early diagnosis of AD, the model outperforms traditional deep learning models trained on original datasets in terms of accuracy and sensitivity. This progress not only proves the potential application of deep learning technology in the field of medical image analysis but also offers a new technical approach to the early detection and diagnosis of Alzheimer's Disease.