干旱区生态环境知识资源中心(Arid): Classification of Alzheimer's Disease with and without Imagery Using Gradient Boosted Machines and ResNet-50

Arid

DOI	10.3390/brainsci9090212
	Classification of Alzheimer's Disease with and without Imagery Using Gradient Boosted Machines and ResNet-50
	Fulton, Lawrence, V 1; Dolezel, Diane 1; Harrop, Jordan 2; Yan, Yan 1; Fulton, Christopher P.3
通讯作者	Fulton, Christopher P.
来源期刊	BRAIN SCIENCES
EISSN	2076-3425
出版年	2019
卷号	9 期号:9
英文摘要	Background. Alzheimer's is a disease for which there is no cure. Diagnosing Alzheimer's disease (AD) early facilitates family planning and cost control. The purpose of this study is to predict the presence of AD using socio-demographic, clinical, and magnetic resonance imaging (MRI) data. Early detection of AD enables family planning and may reduce costs by delaying long-term care. Accurate, non-imagery methods also reduce patient costs. The Open Access Series of Imaging Studies (OASIS-1) cross-sectional MRI data were analyzed. A gradient boosted machine (GBM) predicted the presence of AD as a function of gender, age, education, socioeconomic status (SES), and a mini-mental state exam (MMSE). A residual network with 50 layers (ResNet-50) predicted the clinical dementia rating (CDR) presence and severity from MRI's (multi-class classification). The GBM achieved a mean 91.3% prediction accuracy (10-fold stratified cross validation) for dichotomous CDR using socio-demographic and MMSE variables. MMSE was the most important feature. ResNet-50 using image generation techniques based on an 80% training set resulted in 98.99% three class prediction accuracy on 4139 images (20% validation set) at Epoch 133 and nearly perfect multi-class predication accuracy on the training set (99.34%). Machine learning methods classify AD with high accuracy. GBM models may help provide initial detection based on non-imagery analysis, while ResNet-50 network models might help identify AD patients automatically prior to provider review.
英文关键词	Alzheimer's disease extreme gradient boosting deep residual learning convolutional neural networks machine learning dementia
类型	Article
语种	英语
国家	USA
开放获取类型	gold, Green Published, Green Submitted
收录类别	SCI-E
WOS记录号	WOS:000487745400011
WOS关键词	CLINICAL SCORE PREDICTION ; DEMENTIA ; YOUNG
WOS类目	Neurosciences
WOS研究方向	Neurosciences & Neurology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/214734
作者单位	1.Texas State Univ, Dept Hlth Adm, 601 Univ Dr, San Marcos, TX 78666 USA; 2.Acushnet Holdings Corp, Acushnet, MA 02743 USA; 3.US Air Force, Expt Test Pilot Sch, Edwards AFB, CA 93524 USA
推荐引用方式 GB/T 7714	Fulton, Lawrence, V,Dolezel, Diane,Harrop, Jordan,et al. Classification of Alzheimer's Disease with and without Imagery Using Gradient Boosted Machines and ResNet-50[J],2019,9(9).
APA	Fulton, Lawrence, V,Dolezel, Diane,Harrop, Jordan,Yan, Yan,&Fulton, Christopher P..(2019).Classification of Alzheimer's Disease with and without Imagery Using Gradient Boosted Machines and ResNet-50.BRAIN SCIENCES,9(9).
MLA	Fulton, Lawrence, V,et al."Classification of Alzheimer's Disease with and without Imagery Using Gradient Boosted Machines and ResNet-50".BRAIN SCIENCES 9.9(2019).