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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10575015/ https://www.ncbi.nlm.nih.gov/pubmed/37837101 http://dx.doi.org/10.3390/s23198272 |
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author | Khalil, Kasem Khan Mamun, Mohammad Mahbubur Rahman Sherif, Ahmed Elsersy, Mohamed Said Imam, Ahmad Abdel-Aliem Mahmoud, Mohamed Alsabaan, Maazen |
author_facet | Khalil, Kasem Khan Mamun, Mohammad Mahbubur Rahman Sherif, Ahmed Elsersy, Mohamed Said Imam, Ahmad Abdel-Aliem Mahmoud, Mohamed Alsabaan, Maazen |
author_sort | Khalil, Kasem |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10575015 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-105750152023-10-14 A Federated Learning Model Based on Hardware Acceleration for the Early Detection of Alzheimer’s Disease Khalil, Kasem Khan Mamun, Mohammad Mahbubur Rahman Sherif, Ahmed Elsersy, Mohamed Said Imam, Ahmad Abdel-Aliem Mahmoud, Mohamed Alsabaan, Maazen Sensors (Basel) Article 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. MDPI 2023-10-06 /pmc/articles/PMC10575015/ /pubmed/37837101 http://dx.doi.org/10.3390/s23198272 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Khalil, Kasem Khan Mamun, Mohammad Mahbubur Rahman Sherif, Ahmed Elsersy, Mohamed Said Imam, Ahmad Abdel-Aliem Mahmoud, Mohamed Alsabaan, Maazen A Federated Learning Model Based on Hardware Acceleration for the Early Detection of Alzheimer’s Disease |
title | A Federated Learning Model Based on Hardware Acceleration for the Early Detection of Alzheimer’s Disease |
title_full | A Federated Learning Model Based on Hardware Acceleration for the Early Detection of Alzheimer’s Disease |
title_fullStr | A Federated Learning Model Based on Hardware Acceleration for the Early Detection of Alzheimer’s Disease |
title_full_unstemmed | A Federated Learning Model Based on Hardware Acceleration for the Early Detection of Alzheimer’s Disease |
title_short | A Federated Learning Model Based on Hardware Acceleration for the Early Detection of Alzheimer’s Disease |
title_sort | federated learning model based on hardware acceleration for the early detection of alzheimer’s disease |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10575015/ https://www.ncbi.nlm.nih.gov/pubmed/37837101 http://dx.doi.org/10.3390/s23198272 |
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