Deep learning methods to detect Alzheimer's disease from MRI: A systematic review
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F23%3A10253060" target="_blank" >RIV/61989100:27240/23:10253060 - isvavai.cz</a>
Result on the web
<a href="https://onlinelibrary.wiley.com/doi/10.1111/exsy.13463" target="_blank" >https://onlinelibrary.wiley.com/doi/10.1111/exsy.13463</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1111/exsy.13463" target="_blank" >10.1111/exsy.13463</a>
Alternative languages
Result language
angličtina
Original language name
Deep learning methods to detect Alzheimer's disease from MRI: A systematic review
Original language description
Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative condition in the brain that affects memory, thinking, and behaviour. To overcome this problem, which according to the World Health Organization, is on the rise, creating strategies is essential to identify and predict the disease in its early stages before clinical manifestation. In addition to cognitive and mental tests, neuroimaging is promising in this field, especially in assessing brain matter loss. Therefore, computer-aided diagnosis systems have been imposed as fundamental tools to help imaging technicians as the diagnosis becomes less subjective and time-consuming. Thus, machine learning and deep learning (DL) techniques have come into play. In recent years, articles addressing the topic of Alzheimer's diagnosis through DL models are increasingly popular, with an exponential increase from year to year with increasingly higher accuracy values. However, the disease classification remains a challenging and progressing issue, not only in distinguishing between healthy controls and AD patients but mainly in differentiating intermediate stages such as mild cognitive impairment. Therefore, there is a need to develop more valuable and innovative techniques. This article presents an up-to-date systematic review of deep models to detect AD and its intermediate phase by evaluating magnetic resonance images. The DL models chosen by different authors are analysed, as well as their approaches regarding the used dataset and the data pre-processing and analysis techniques. (C) 2023 John Wiley & Sons Ltd.
Czech name
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Czech description
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Classification
Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
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OECD FORD branch
20200 - Electrical engineering, Electronic engineering, Information engineering
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Expert Systems
ISSN
0266-4720
e-ISSN
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Volume of the periodical
Neuveden
Issue of the periodical within the volume
září 2023
Country of publishing house
US - UNITED STATES
Number of pages
23
Pages from-to
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UT code for WoS article
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EID of the result in the Scopus database
2-s2.0-85173507705