Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Different techniques for Alzheimer’s disease classification using brain images: a study

Different techniques for Alzheimer’s disease classification using brain images: a study Alzheimer’s disease (AD) is a kind of dementia that is mostly experienced by people who are in the age of early 60s. In AD, brain cells that are responsible for forming memories and cognitive decisions, get affected which causes overall gray matter shrinkage in the human brain. Since AD patients are growing exponentially in the world, researchers are trying to develop an accurate mechanism for diagnosing the disease using brain images. In this paper, several research articles on AD classification are analyzed along with detailed observations. We have summarized as well as compared the research articles based on their classification performance. Although all the reviewed articles have the potential to classify AD, still there lies major future challenges. Among all the reviewed papers, it is found that the recent deep neural network-based classification techniques can produce the most promising results with an average performance rate of 93%. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Multimedia Information Retrieval Springer Journals

Different techniques for Alzheimer’s disease classification using brain images: a study

Download PDF
20 pages

Loading next page...
 
/lp/springer-journals/different-techniques-for-alzheimer-s-disease-classification-using-UE7ARY8dZ0

References (97)

Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021
ISSN
2192-6611
eISSN
2192-662X
DOI
10.1007/s13735-021-00210-9
Publisher site
See Article on Publisher Site

Abstract

Alzheimer’s disease (AD) is a kind of dementia that is mostly experienced by people who are in the age of early 60s. In AD, brain cells that are responsible for forming memories and cognitive decisions, get affected which causes overall gray matter shrinkage in the human brain. Since AD patients are growing exponentially in the world, researchers are trying to develop an accurate mechanism for diagnosing the disease using brain images. In this paper, several research articles on AD classification are analyzed along with detailed observations. We have summarized as well as compared the research articles based on their classification performance. Although all the reviewed articles have the potential to classify AD, still there lies major future challenges. Among all the reviewed papers, it is found that the recent deep neural network-based classification techniques can produce the most promising results with an average performance rate of 93%.

Journal

International Journal of Multimedia Information Retrieval – Springer Journals

Published: Dec 1, 2021

Keywords: Alzheimer’s disease (AD); Artificial neural network (ANN); Support vector machine (SVM); Random forest (RF); K-nearest neighbor (KNN); Magnetic resonance imaging (MRI)

There are no references for this article.