Machine Learning for Alzheimer's Disease Detection Based on Neuroimaging techniques: A Review

被引：1

作者：

Gharaibeh, Maha ^{[1
]}

Elhies, Mwaffaq ^{[1
]}

Almahmoud, Mothanna ^{[2
]}

Abualigah, Sayel ^{[2
]}

Elayan, Omar ^{[2
]}

机构：

[1] Jordan Univ Sci & Technol, Dept Diagnost Radiol, Irbid, Jordan

[2] Jordan Univ Sci & Technol, Dept Comp Informat Syst, Irbid, Jordan

来源：

2022 13TH INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION SYSTEMS (ICICS) | 2022年

关键词：

Alzheimer's Disease; Early Detection; Alzheimer's Databases; Neuroimaging; Machine Learning; Deep Learning; COGNITIVE-DECLINE; BRAIN METABOLISM; TOMOGRAPHY; CLASSIFICATION; LIMITATIONS; DIAGNOSIS; DEMENTIA; HEALTH; CARE; MRI;

D O I：

10.1109/ICICS55353.2022.9811143

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Disease detection became one of the most important applications, especially with the rapid development of artificial intelligence techniques in the medical field. Alzheimer's disease is considered as one of the irreversible disorders that infect the human brain, where cognitive performance declined, gradually. This paper present and discuss machine learning approaches for Alzheimer's disease detection based on the neuroimaging modalities. Based on the revision, it shows that the utilization of different modalities, the availability of the scans, and the optimization of machine learning architectures played the main role to devise an accurate detection method for Alzheimer's disease.

引用

页码：426 / 431

页数：6

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