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Arid

DOI	10.1109/ACCESS.2019.2932786
	A Data Augmentation-Based Framework to Handle Class Imbalance Problem for Alzheimer's Stage Detection
	Afzal, Sitara 1; Maqsood, Muazzam 1; Nazir, Faria 2; Khan, Umair 1; Aadil, Farhan 1; Awan, Khalid M.1; Mehmood, Irfan 3; Song, Oh-Young 4
通讯作者	Maqsood, Muazzam ; Song, Oh-Young
来源期刊	IEEE ACCESS
ISSN	2169-3536
出版年	2019
卷号	7 页码:115528-115539
英文摘要	Alzheimer's Disease (AD) is the most common form of dementia. It gradually increases from mild stage to severe, affecting the ability to perform common daily tasks without assistance. It is a neurodegenerative illness, presently having no specified cure. Computer-Aided Diagnostic Systems have played an important role to help physicians to identify AD. However, the diagnosis of AD into its four stages; No Dementia, Very Mild Dementia, Mild Dementia, and Moderate Dementia remains an open research area. Deep learning assisted computer-aided solutions are proved to be more useful because of their high accuracy. However, the most common problem with deep learning architecture is that large training data is required. Furthermore, the samples should be evenly distributed among the classes to avoid the class imbalance problem. The publicly available dataset (OASIS) has serious class imbalance problem. In this research, we employed a transfer learning-based technique using data augmentation for 3D Magnetic Resonance Imaging (MRI) views from OASIS dataset. The accuracy of the proposed model utilizing a single view of the brain MRI is 98.41% while using 3D-views is 95.11%. The proposed system outperformed the existing techniques for Alzheimer disease stages.
英文关键词	Transfer learning AlexNet convolutional neural network Alzheimer's disease augmentation
类型	Article
语种	英语
国家	Pakistan ; England ; South Korea
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000484224000001
WOS关键词	MILD COGNITIVE IMPAIRMENT ; DISEASE CLASSIFICATION ; NEURAL-NETWORKS ; FEATURE-RANKING ; STRUCTURAL MRI ; RECOGNITION ; DIAGNOSIS ; SELECTION ; IMAGES
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS研究方向	Computer Science ; Engineering ; Telecommunications
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/216237
作者单位	1.COMSATS Univ Islamabad, Dept Comp Sci, Attock Campus, Attock 43600, Pakistan; 2.Capital Univ Sci & Technol, Dept Comp Sci, Islamabad 45750, Pakistan; 3.Univ Bradford, Fac Engn & Informat, Dept Media Design & Technol, Bradford BD7 1DP, W Yorkshire, England; 4.Sejong Univ, Dept Software, Seoul 05006, South Korea
推荐引用方式 GB/T 7714	Afzal, Sitara,Maqsood, Muazzam,Nazir, Faria,et al. A Data Augmentation-Based Framework to Handle Class Imbalance Problem for Alzheimer's Stage Detection[J],2019,7:115528-115539.
APA	Afzal, Sitara.,Maqsood, Muazzam.,Nazir, Faria.,Khan, Umair.,Aadil, Farhan.,...&Song, Oh-Young.(2019).A Data Augmentation-Based Framework to Handle Class Imbalance Problem for Alzheimer's Stage Detection.IEEE ACCESS,7,115528-115539.
MLA	Afzal, Sitara,et al."A Data Augmentation-Based Framework to Handle Class Imbalance Problem for Alzheimer's Stage Detection".IEEE ACCESS 7(2019):115528-115539.