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

被引：3
|
作者：
Hazarika, Ruhul Amin [1 ]
Abraham, Ajith [2 ]
Sur, Samarendra Nath [3 ]
Maji, Arnab Kumar [1 ]
Kandar, Debdatta [1 ]
机构：
[1] North Eastern Hill Univ, Dept Informat Technol, Shillong 793022, Meghalaya, India
[2] Sci Network Innovat & Res Excellence Auburn, Machine Intelligence Res Labs MIR Labs, Auburn, WA 98071 USA
[3] Sikkim Manipal Univ, Sikkim Manipal Inst Technol, Dept Elect & Commun Engn, Majitar 737136, Sikkim, India
来源：
INTERNATIONAL JOURNAL OF MULTIMEDIA INFORMATION RETRIEVAL | 2021年 / 10卷 / 04期
关键词：
Alzheimer’ s disease (AD); Artificial neural network (ANN); Support vector machine (SVM); Random forest (RF); K-nearest neighbor (KNN); Magnetic resonance imaging (MRI); TRANSCRANIAL MAGNETIC STIMULATION; FEATURE-SELECTION; NEURAL-NETWORKS; GRAY-MATTER; DIAGNOSIS; PREDICTION;
D O I：
10.1007/s13735-021-00210-9
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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%.
引用
收藏
页码：199 / 218
页数：20
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