Machine Learning Techniques for the Diagnosis of Alzheimer's Disease: A Review

被引：248

作者：

Tanveer, M. ^{[1
]}

Richhariya, B. ^{[1
]}

Khan, R. U. ^{[1
]}

Rashid, A. H. ^{[1
,2
]}

Khanna, P. ^{[3
]}

Prasad, M. ^{[4
]}

Lin, C. T. ^{[4
]}

机构：

[1] Indian Inst Technol Indore, Discipline Math, Indore 453552, India

[2] Natl Inst Sci & Technol, Sch Comp Sci & Engn, Berhampur 761008, Odisha, India

[3] PDPM Indian Inst Informat Technol Design & Mfg, Jabalpur 482005, India

[4] Univ Technol Sydney, Ctr Artificial Intelligence, Sch Comp Sci, FEIT, Sydney, NSW, Australia

来源：

ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS | 2020年 / 16卷 / 01期

关键词：

Magnetic resonance imaging (MRI); positron emission tomography (PET); diffusion tensor imaging (DTI); mild cognitive impairment (MCI); MILD COGNITIVE IMPAIRMENT; SUPPORT VECTOR MACHINE; INDEPENDENT COMPONENT ANALYSIS; ARTIFICIAL NEURAL-NETWORKS; COMPUTER-AIDED DIAGNOSIS; RESTING-STATE FMRI; FEATURE-SELECTION; BRAIN IMAGES; MULTIMODAL CLASSIFICATION; AUTOMATIC CLASSIFICATION;

D O I：

10.1145/3344998

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Alzheimer's disease is an incurable neurodegenerative disease primarily affecting the elderly population. Efficient automated techniques are needed for early diagnosis of Alzheimer's. Many novel approaches are proposed by researchers for classification of Alzheimer's disease. However, to develop more efficient learning techniques, better understanding of the work done on Alzheimer's is needed. Here, we provide a review on 165 papers from 2005 to 2019, using various feature extraction and machine learning techniques. The machine learning techniques are surveyed under three main categories: support vector machine (SVM), artificial neural network (ANN), and deep learning (DL) and ensemble methods. We present a detailed review on these three approaches for Alzheimer's with possible future directions.

引用

页数：35

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