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Hybridized Deep Learning Approach for Detecting Alzheimer’s Disease
Alzheimer’s disease (AD) is mainly a neurodegenerative sickness. The primary characteristics are neuronal atrophy, amyloid deposition, and cognitive, behavioral, and psychiatric disorders. Numerous machine learning (ML) algorithms have been investigated and applied to AD identification over the past...
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/PMC9855764/ https://www.ncbi.nlm.nih.gov/pubmed/36672656 http://dx.doi.org/10.3390/biomedicines11010149 |
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author | Balaji, Prasanalakshmi Chaurasia, Mousmi Ajay Bilfaqih, Syeda Meraj Muniasamy, Anandhavalli Alsid, Linda Elzubir Gasm |
author_facet | Balaji, Prasanalakshmi Chaurasia, Mousmi Ajay Bilfaqih, Syeda Meraj Muniasamy, Anandhavalli Alsid, Linda Elzubir Gasm |
author_sort | Balaji, Prasanalakshmi |
collection | PubMed |
description | Alzheimer’s disease (AD) is mainly a neurodegenerative sickness. The primary characteristics are neuronal atrophy, amyloid deposition, and cognitive, behavioral, and psychiatric disorders. Numerous machine learning (ML) algorithms have been investigated and applied to AD identification over the past decades, emphasizing the subtle prodromal stage of mild cognitive impairment (MCI) to assess critical features that distinguish the disease’s early manifestation and instruction for early detection and treatment. Identifying early MCI (EMCI) remains challenging due to the difficulty in distinguishing patients with cognitive normality from those with MCI. As a result, most classification algorithms for these two groups perform poorly. This paper proposes a hybrid Deep Learning Approach for the early detection of Alzheimer’s disease. A method for early AD detection using multimodal imaging and Convolutional Neural Network with the Long Short-term memory algorithm combines magnetic resonance imaging (MRI), positron emission tomography (PET), and standard neuropsychological test scores. The proposed methodology updates the learning weights, and Adam’s optimization is used to increase accuracy. The system has an unparalleled accuracy of 98.5% in classifying cognitively normal controls from EMCI. These results imply that deep neural networks may be trained to automatically discover imaging biomarkers indicative of AD and use them to identify the illness accurately. |
format | Online Article Text |
id | pubmed-9855764 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-98557642023-01-21 Hybridized Deep Learning Approach for Detecting Alzheimer’s Disease Balaji, Prasanalakshmi Chaurasia, Mousmi Ajay Bilfaqih, Syeda Meraj Muniasamy, Anandhavalli Alsid, Linda Elzubir Gasm Biomedicines Article Alzheimer’s disease (AD) is mainly a neurodegenerative sickness. The primary characteristics are neuronal atrophy, amyloid deposition, and cognitive, behavioral, and psychiatric disorders. Numerous machine learning (ML) algorithms have been investigated and applied to AD identification over the past decades, emphasizing the subtle prodromal stage of mild cognitive impairment (MCI) to assess critical features that distinguish the disease’s early manifestation and instruction for early detection and treatment. Identifying early MCI (EMCI) remains challenging due to the difficulty in distinguishing patients with cognitive normality from those with MCI. As a result, most classification algorithms for these two groups perform poorly. This paper proposes a hybrid Deep Learning Approach for the early detection of Alzheimer’s disease. A method for early AD detection using multimodal imaging and Convolutional Neural Network with the Long Short-term memory algorithm combines magnetic resonance imaging (MRI), positron emission tomography (PET), and standard neuropsychological test scores. The proposed methodology updates the learning weights, and Adam’s optimization is used to increase accuracy. The system has an unparalleled accuracy of 98.5% in classifying cognitively normal controls from EMCI. These results imply that deep neural networks may be trained to automatically discover imaging biomarkers indicative of AD and use them to identify the illness accurately. MDPI 2023-01-06 /pmc/articles/PMC9855764/ /pubmed/36672656 http://dx.doi.org/10.3390/biomedicines11010149 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 Balaji, Prasanalakshmi Chaurasia, Mousmi Ajay Bilfaqih, Syeda Meraj Muniasamy, Anandhavalli Alsid, Linda Elzubir Gasm Hybridized Deep Learning Approach for Detecting Alzheimer’s Disease |
title | Hybridized Deep Learning Approach for Detecting Alzheimer’s Disease |
title_full | Hybridized Deep Learning Approach for Detecting Alzheimer’s Disease |
title_fullStr | Hybridized Deep Learning Approach for Detecting Alzheimer’s Disease |
title_full_unstemmed | Hybridized Deep Learning Approach for Detecting Alzheimer’s Disease |
title_short | Hybridized Deep Learning Approach for Detecting Alzheimer’s Disease |
title_sort | hybridized deep learning approach for detecting alzheimer’s disease |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9855764/ https://www.ncbi.nlm.nih.gov/pubmed/36672656 http://dx.doi.org/10.3390/biomedicines11010149 |
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