干旱区生态环境知识资源中心(Arid): A Deep Siamese Convolution Neural Network for Multi-Class Classification of Alzheimer Disease

Arid

DOI	10.3390/brainsci10020084
	A Deep Siamese Convolution Neural Network for Multi-Class Classification of Alzheimer Disease
	Mehmood, Atif 1; Maqsood, Muazzam 2; Bashir, Muzaffar 3; Yang Shuyuan 1
通讯作者	Yang Shuyuan
来源期刊	BRAIN SCIENCES
EISSN	2076-3425
出版年	2020
卷号	10 期号:2
英文摘要	Alzheimer's disease (AD) may cause damage to the memory cells permanently, which results in the form of dementia. The diagnosis of Alzheimer's disease at an early stage is a problematic task for researchers. For this, machine learning and deep convolutional neural network (CNN) based approaches are readily available to solve various problems related to brain image data analysis. In clinical research, magnetic resonance imaging (MRI) is used to diagnose AD. For accurate classification of dementia stages, we need highly discriminative features obtained from MRI images. Recently advanced deep CNN-based models successfully proved their accuracy. However, due to a smaller number of image samples available in the datasets, there exist problems of over-fitting hindering the performance of deep learning approaches. In this research, we developed a Siamese convolutional neural network (SCNN) model inspired by VGG-16 (also called Oxford Net) to classify dementia stages. In our approach, we extend the insufficient and imbalanced data by using augmentation approaches. Experiments are performed on a publicly available dataset open access series of imaging studies (OASIS), by using the proposed approach, an excellent test accuracy of 99.05% is achieved for the classification of dementia stages. We compared our model with the state-of-the-art models and discovered that the proposed model outperformed the state-of-the-art models in terms of performance, efficiency, and accuracy.
英文关键词	Alzheimer's disease dementia convolutional neural network classification deep learning batch normalization
类型	Article
语种	英语
国家	Peoples R China ; Pakistan
开放获取类型	Green Published, gold
收录类别	SCI-E
WOS记录号	WOS:000519243400049
WOS关键词	MRI ; CLASSIFIERS ; PREDICTION ; DIAGNOSIS ; SELECTION
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/2XILL650/314203
作者单位	1.Xidian Univ, Sch Artificial Intelligence, 2 South Taibai Rd, Xian 710071, Peoples R China; 2.COMSATS Univ Islamabad, Dept Comp Sci, Attock Campus, Attock 43600, Pakistan; 3.Univ Punjab, Dept Phys, Lahore 54590, Pakistan
推荐引用方式 GB/T 7714	Mehmood, Atif,Maqsood, Muazzam,Bashir, Muzaffar,et al. A Deep Siamese Convolution Neural Network for Multi-Class Classification of Alzheimer Disease[J],2020,10(2).
APA	Mehmood, Atif,Maqsood, Muazzam,Bashir, Muzaffar,&Yang Shuyuan.(2020).A Deep Siamese Convolution Neural Network for Multi-Class Classification of Alzheimer Disease.BRAIN SCIENCES,10(2).
MLA	Mehmood, Atif,et al."A Deep Siamese Convolution Neural Network for Multi-Class Classification of Alzheimer Disease".BRAIN SCIENCES 10.2(2020).