干旱区生态环境知识资源中心(Arid): Using Three-dimensional Convolutional Neural Networks for Alzheimer's Disease Diagnosis

Arid

DOI	10.18494/SAM.2021.3512
	Using Three-dimensional Convolutional Neural Networks for Alzheimer's Disease Diagnosis
	Lin, Cheng-Jian; Lin, Cheng-Wei
通讯作者	Lin, CJ (corresponding author), Natl Chin Yi Univ Technol, Dept Comp Sci & Informat Engn, Taichung 411, Taiwan. ; Lin, CJ (corresponding author), Natl Taichung Univ Sci & Technol, Coll Intelligence, Taichung 404, Taiwan.
来源期刊	SENSORS AND MATERIALS
ISSN	0914-4935
出版年	2021
卷号	33 期号:10 页码:3399-3413
英文摘要	Alzheimer's disease (AD) is an irreversible neurodegenerative disease. Pathology shows atrophy of brain tissue, senile plaques, neurofibrillary tangles, and so forth. Magnetic resonance imaging (MRI) is the most sensitive brain imaging method in a clinic, which provides detailed anatomical structure information of the brain and is commonly studied with pattern recognition methods for AD diagnosis. Most existing methods extract hand-crafted imaging features or brain region- of-interest images to train a classifier to recognize AD. In this study, a threedimensional convolutional neural network (3D-CNN) is implemented to detect AD. The proposed network is trained with 3D magnetic resonance ( MR) images to extract the spatial features. A uniform experimental design is used to optimize the network parameters and improve the 3D-CNN performance. To ensure satisfactory performance on a small amount of training data, a transfer learning technology is proposed to improve the recognition rate of the 3D-CNN. In addition, the uniform experimental design ( UED) method is used to determine the optimal parameter combination of the network and improve the 3D-CNN performance. The validation data in this study are from the Open Access Series of Imaging Studies (OASIS), where the OASIS-1 data set is used as the original data set and the 3D-CNN is trained as the pretraining model. Experimental results show that when 10, 30, 60, and 90% of the OASIS-2 data set are used to train the pre-trained 3D-CNN, the average accuracy reaches 74.66, 86.99, 94.58, and 97.02%, respectively. In addition, compared with the original manual design parameters, the proposed 3D-CNN with the best parameter combination improves the recognition rate by 2.07 percentage points.
英文关键词	Alzheimer's disease magnetic resonance imaging convolutional neural network uniform experimental design transfer learning
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000706173600002
WOS关键词	OPEN ACCESS SERIES ; MRI DATA ; DESIGN
WOS类目	Instruments & Instrumentation ; Materials Science, Multidisciplinary
WOS研究方向	Instruments & Instrumentation ; Materials Science
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/364687
作者单位	[Lin, Cheng-Jian; Lin, Cheng-Wei] Natl Chin Yi Univ Technol, Dept Comp Sci & Informat Engn, Taichung 411, Taiwan; [Lin, Cheng-Jian] Natl Taichung Univ Sci & Technol, Coll Intelligence, Taichung 404, Taiwan
推荐引用方式 GB/T 7714	Lin, Cheng-Jian,Lin, Cheng-Wei. Using Three-dimensional Convolutional Neural Networks for Alzheimer's Disease Diagnosis[J],2021,33(10):3399-3413.
APA	Lin, Cheng-Jian,&Lin, Cheng-Wei.(2021).Using Three-dimensional Convolutional Neural Networks for Alzheimer's Disease Diagnosis.SENSORS AND MATERIALS,33(10),3399-3413.
MLA	Lin, Cheng-Jian,et al."Using Three-dimensional Convolutional Neural Networks for Alzheimer's Disease Diagnosis".SENSORS AND MATERIALS 33.10(2021):3399-3413.