Oraby Sarah, Emran Ahmed, El-Saghir Basel, Mohsen Saeed
Department of Electrical Engineering, Faculty of Engineering, Al-Azhar University, Cairo, Egypt.
Department of Artificial Intelligence Engineering, Faculty of Computer Science and Engineering, King Salman International University (KSIU), South Sinai, 46511, Egypt.
Sci Rep. 2025 Apr 13;15(1):12727. doi: 10.1038/s41598-025-94677-9.
In this paper, we propose a deep super-resolution generative adversarial network (DSR-GAN) combined with a convolutional neural network (CNN) model designed to classify four stages of Alzheimer's disease (AD): Mild Dementia (MD), Moderate Dementia (MOD), Non-Demented (ND), and Very Mild Dementia (VMD). The proposed DSR-GAN is implemented using a PyTorch library and uses a dataset of 6,400 MRI images. A super-resolution (SR) technique is applied to enhance the clarity and detail of the images, allowing the DSR-GAN to refine particular image features. The CNN model undergoes hyperparameter optimization and incorporates data augmentation strategies to maximize its efficiency. The normalized error matrix and area under ROC curve are used experimentally to evaluate the CNN's performance which achieved a testing accuracy of 99.22%, an area under the ROC curve of 100%, and an error rate of 0.0516. Also, the performance of the DSR-GAN is assessed using three different metrics: structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), and multi-scale structural similarity index measure (MS-SSIM). The achieved SSIM score of 0.847, while the PSNR and MS-SSIM percentage are 29.30 dB and 96.39%, respectively. The combination of the DSR-GAN and CNN models provides a rapid and precise method to distinguish between various stages of Alzheimer's disease, potentially aiding professionals in the screening of AD cases.
在本文中，我们提出了一种深度超分辨率生成对抗网络（DSR-GAN），它与一个卷积神经网络（CNN）模型相结合，旨在对阿尔茨海默病（AD）的四个阶段进行分类：轻度痴呆（MD）、中度痴呆（MOD）、非痴呆（ND）和极轻度痴呆（VMD）。所提出的DSR-GAN使用PyTorch库实现，并使用一个包含6400张MRI图像的数据集。应用超分辨率（SR）技术来增强图像的清晰度和细节，使DSR-GAN能够细化特定的图像特征。CNN模型进行了超参数优化，并纳入了数据增强策略以最大化其效率。实验中使用归一化误差矩阵和ROC曲线下面积来评估CNN的性能，其测试准确率达到99.22%，ROC曲线下面积为100%，错误率为0.0516。此外，使用三种不同的指标评估DSR-GAN的性能：结构相似性指数测量（SSIM）、峰值信噪比（PSNR）和多尺度结构相似性指数测量（MS-SSIM）。实现的SSIM分数为0.847，而PSNR和MS-SSIM百分比分别为29.30 dB和96.39%。DSR-GAN和CNN模型的结合提供了一种快速且精确的方法来区分阿尔茨海默病的各个阶段，可能有助于专业人员筛查AD病例。
