Chen Xin, Zeng Deze, Ullah Rahmat, Nawaz Rab, Xu Jiafeng, Arslan Tughrul
School of Computer Science, China University of Geosciences, Wuhan, 430074, China.
School of Automation, China University of Geosciences, Wuhan, 430074, China.
Neural Netw. 2025 Jan;181:106778. doi: 10.1016/j.neunet.2024.106778. Epub 2024 Oct 5.
Over 50 million people globally suffer from Alzheimer's disease (AD), emphasizing the need for efficient, early diagnostic tools. Traditional methods like Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scans are expensive, bulky, and slow. Microwave-based techniques offer a cost-effective, non-invasive, and portable solution, diverging from conventional neuroimaging practices. This article introduces a deep learning approach for monitoring AD , using realistic numerical brain phantoms to simulate scattered signals via the CST Studio Suite. The obtained data is preprocessed using normalization, standardization, and outlier removal to ensure data integrity. Furthermore, we propose a novel data augmentation technique to enrich the dataset across various AD stages. Our deep learning approach combines Recursive Feature Elimination (RFE) with Principal Component Analysis (PCA) and Autoencoders (AE) for optimal feature selection. Convolution Neural Network (CNN) is combined with Gated Recurrent Unit (GRU), Bidirectional Long Short Term Memory (Bidirectional-LSTM), and Long Short-Term Memory (LSTM) to improve classification performance. The integration of RFE-PCA-AE significantly elevates performance, with the CNN+GRU model achieving an 87% accuracy rate, thus outperforming existing studies.
全球超过5000万人患有阿尔茨海默病（AD），这凸显了对高效、早期诊断工具的需求。传统方法如磁共振成像（MRI）和计算机断层扫描（CT）扫描既昂贵、体积庞大又耗时。基于微波的技术提供了一种经济高效、非侵入性且便携的解决方案，与传统神经成像方法不同。本文介绍了一种用于监测AD的深度学习方法，使用逼真的数字脑模型通过CST Studio Suite模拟散射信号。对获得的数据进行归一化、标准化和异常值去除等预处理，以确保数据完整性。此外，我们提出了一种新颖的数据增强技术，以丰富各个AD阶段的数据集。我们的深度学习方法将递归特征消除（RFE）与主成分分析（PCA）和自动编码器（AE）相结合，以实现最佳特征选择。卷积神经网络（CNN）与门控循环单元（GRU）、双向长短期记忆（Bidirectional-LSTM）和长短期记忆（LSTM）相结合，以提高分类性能。RFE-PCA-AE的集成显著提高了性能，CNN+GRU模型的准确率达到87%，从而优于现有研究。
