A Federated Learning Model Based on Hardware Acceleration for the Early Detection of Alzheimer’s Disease

A Federated Learning Model Based on Hardware Acceleration for the Early Detection of Alzheimer’s Disease

Alzheimer’s disease (AD) is a progressive illness with a slow start that lasts many years; the disease’s consequences are devastating to the patient and the patient’s family. If detected early, the disease’s impact and prognosis can be altered significantly. Blood biosamples are often employed in si...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 23; no. 19; p. 8272
Main Authors: Khalil, Kasem, Khan Mamun, Mohammad Mahbubur Rahman, Sherif, Ahmed, Elsersy, Mohamed Said, Imam, Ahmad Abdel-Aliem, Mahmoud, Mohamed, Alsabaan, Maazen
Format: Journal Article
Language:English
Published: Basel MDPI AG 01-10-2023
MDPI
Subjects:
Accuracy
Advertising executives
Algorithms
Alzheimer's disease
Artificial intelligence
Biomarkers
blood bio-samples
Brain research
Computational linguistics
Datasets
Dementia
Diagnostic tests
Digital integrated circuits
Disease
early diagnosis
federated learning
hardware acceleration
Language processing
Magnetic resonance imaging
Medical imaging
Natural language interfaces
Neural networks
Neuroimaging
Pathology
Patients
Peptides
Performance evaluation
Privacy
Prognosis
Proteins
Public health
Simulation methods
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Summary:Alzheimer’s disease (AD) is a progressive illness with a slow start that lasts many years; the disease’s consequences are devastating to the patient and the patient’s family. If detected early, the disease’s impact and prognosis can be altered significantly. Blood biosamples are often employed in simple medical testing since they are cost-effective and easy to collect and analyze. This research provides a diagnostic model for Alzheimer’s disease based on federated learning (FL) and hardware acceleration using blood biosamples. We used blood biosample datasets provided by the ADNI website to compare and evaluate the performance of our models. FL has been used to train a shared model without sharing local devices’ raw data with a central server to preserve privacy. We developed a hardware acceleration approach for building our FL model so that we could speed up the training and testing procedures. The VHDL hardware description language and an Altera 10 GX FPGA are utilized to construct the hardware-accelerator approach. The results of the simulations reveal that the proposed methods achieve accuracy and sensitivity for early detection of 89% and 87%, respectively, while simultaneously requiring less time to train than other algorithms considered to be state-of-the-art. The proposed algorithms have a power consumption ranging from 35 to 39 mW, which qualifies them for use in limited devices. Furthermore, the result shows that the proposed method has a lower inference latency (61 ms) than the existing methods with fewer resources.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s23198272