Farhatullah and Chen, Xin and Zeng, Deze and Ullah, Rahmat and Nawaz, Rab and Xu, Jiafeng and Arslan, Tughrul (2025) A deep learning approach for non-invasive Alzheimer's monitoring using microwave radar data. Neural Networks, 181. p. 106778. DOI https://doi.org/10.1016/j.neunet.2024.106778
Farhatullah and Chen, Xin and Zeng, Deze and Ullah, Rahmat and Nawaz, Rab and Xu, Jiafeng and Arslan, Tughrul (2025) A deep learning approach for non-invasive Alzheimer's monitoring using microwave radar data. Neural Networks, 181. p. 106778. DOI https://doi.org/10.1016/j.neunet.2024.106778
Farhatullah and Chen, Xin and Zeng, Deze and Ullah, Rahmat and Nawaz, Rab and Xu, Jiafeng and Arslan, Tughrul (2025) A deep learning approach for non-invasive Alzheimer's monitoring using microwave radar data. Neural Networks, 181. p. 106778. DOI https://doi.org/10.1016/j.neunet.2024.106778
Abstract
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.
Item Type: | Article |
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Uncontrolled Keywords: | Alzheimer’s disease; Classification; Deep learning; Data augmentation; Microwave scattering; Signal processing |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 29 Oct 2024 11:15 |
Last Modified: | 30 Oct 2024 17:40 |
URI: | http://repository.essex.ac.uk/id/eprint/39375 |
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