Alzheimer's diseases detection by using deep learning algorithms: a mini-review

Al-Shoukry, Suhad and Rassem, Taha H. and Makbol, Nasrin M. (2020) Alzheimer's diseases detection by using deep learning algorithms: a mini-review. IEEE Access, 8. 77131 - 77141. ISSN 2169-3536. (Published)

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The accurate diagnosis of Alzheimer’s disease (AD) plays an important role in patient treatment, especially at the disease’s early stages, because risk awareness allows the patients to undergo preventive measures even before the occurrence of irreversible brain damage. Although many recent studies have used computers to diagnose AD, most machine detection methods are limited by congenital observations. AD can be diagnosed-but not predicted-at its early stages, as prediction is only applicable before the disease manifests itself. Deep Learning (DL) has become a common technique for the early diagnosis of AD. Here, we briefly review some of the important literature on AD and explore how DL can help researchers diagnose the disease at its early stages.

Item Type: Article
Additional Information: Indexed by Scopus & WOS
Uncontrolled Keywords: Alzheimer’s disease; Deep learning; Early stage detection and diagnosis
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Faculty/Division: Faculty of Computing
Depositing User: Dr. Taha Hussein Alaaldeen Rassem
Date Deposited: 14 Jul 2020 02:10
Last Modified: 14 Jul 2020 02:10
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