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Deep Learning Techniques for the Effective Prediction of Alzheimer’s Disease: A Comprehensive Review
“Alzheimer’s disease” (AD) is a neurodegenerative disorder in which the memory shrinks and neurons die. “Dementia” is described as a gradual decline in mental, psychological, and interpersonal qualities that hinders a person’s ability to function autonomously. AD is the most common degenerative brai...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9601959/ https://www.ncbi.nlm.nih.gov/pubmed/36292289 http://dx.doi.org/10.3390/healthcare10101842 |
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author | Shastry, K Aditya Vijayakumar, V V, Manoj Kumar M B A, Manjunatha B N, Chandrashekhar |
author_facet | Shastry, K Aditya Vijayakumar, V V, Manoj Kumar M B A, Manjunatha B N, Chandrashekhar |
author_sort | Shastry, K Aditya |
collection | PubMed |
description | “Alzheimer’s disease” (AD) is a neurodegenerative disorder in which the memory shrinks and neurons die. “Dementia” is described as a gradual decline in mental, psychological, and interpersonal qualities that hinders a person’s ability to function autonomously. AD is the most common degenerative brain disease. Among the first signs of AD are missing recent incidents or conversations. “Deep learning” (DL) is a type of “machine learning” (ML) that allows computers to learn by doing, much like people do. DL techniques can attain cutting-edge precision, beating individuals in certain cases. A large quantity of tagged information with multi-layered “neural network” architectures is used to perform analysis. Because significant advancements in computed tomography have resulted in sizable heterogeneous brain signals, the use of DL for the timely identification as well as automatic classification of AD has piqued attention lately. With these considerations in mind, this paper provides an in-depth examination of the various DL approaches and their implementations for the identification and diagnosis of AD. Diverse research challenges are also explored, as well as current methods in the field. |
format | Online Article Text |
id | pubmed-9601959 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96019592022-10-27 Deep Learning Techniques for the Effective Prediction of Alzheimer’s Disease: A Comprehensive Review Shastry, K Aditya Vijayakumar, V V, Manoj Kumar M B A, Manjunatha B N, Chandrashekhar Healthcare (Basel) Review “Alzheimer’s disease” (AD) is a neurodegenerative disorder in which the memory shrinks and neurons die. “Dementia” is described as a gradual decline in mental, psychological, and interpersonal qualities that hinders a person’s ability to function autonomously. AD is the most common degenerative brain disease. Among the first signs of AD are missing recent incidents or conversations. “Deep learning” (DL) is a type of “machine learning” (ML) that allows computers to learn by doing, much like people do. DL techniques can attain cutting-edge precision, beating individuals in certain cases. A large quantity of tagged information with multi-layered “neural network” architectures is used to perform analysis. Because significant advancements in computed tomography have resulted in sizable heterogeneous brain signals, the use of DL for the timely identification as well as automatic classification of AD has piqued attention lately. With these considerations in mind, this paper provides an in-depth examination of the various DL approaches and their implementations for the identification and diagnosis of AD. Diverse research challenges are also explored, as well as current methods in the field. MDPI 2022-09-23 /pmc/articles/PMC9601959/ /pubmed/36292289 http://dx.doi.org/10.3390/healthcare10101842 Text en © 2022 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 | Review Shastry, K Aditya Vijayakumar, V V, Manoj Kumar M B A, Manjunatha B N, Chandrashekhar Deep Learning Techniques for the Effective Prediction of Alzheimer’s Disease: A Comprehensive Review |
title | Deep Learning Techniques for the Effective Prediction of Alzheimer’s Disease: A Comprehensive Review |
title_full | Deep Learning Techniques for the Effective Prediction of Alzheimer’s Disease: A Comprehensive Review |
title_fullStr | Deep Learning Techniques for the Effective Prediction of Alzheimer’s Disease: A Comprehensive Review |
title_full_unstemmed | Deep Learning Techniques for the Effective Prediction of Alzheimer’s Disease: A Comprehensive Review |
title_short | Deep Learning Techniques for the Effective Prediction of Alzheimer’s Disease: A Comprehensive Review |
title_sort | deep learning techniques for the effective prediction of alzheimer’s disease: a comprehensive review |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9601959/ https://www.ncbi.nlm.nih.gov/pubmed/36292289 http://dx.doi.org/10.3390/healthcare10101842 |
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