干旱区生态环境知识资源中心(Arid): Detection of Alzheimer's disease using grey wolf optimization based clustering algorithm and deep neural network from magnetic resonance images

Arid

DOI	10.1007/s10619-021-07345-y
	Detection of Alzheimer's disease using grey wolf optimization based clustering algorithm and deep neural network from magnetic resonance images
	Suresha, Halebeedu Subbaraya; Parthasarathy, Srirangapatna Sampathkumaran
通讯作者	Suresha, HS (corresponding author), Univ Mysore, PET Res Ctr, Dept ECE, Mysore, Karnataka, India.
来源期刊	DISTRIBUTED AND PARALLEL DATABASES
ISSN	0926-8782
EISSN	1573-7578
出版年	2021-06
英文摘要	The automated magnetic resonance imaging (MRI) processing techniques are gaining more importance in Alzheimer disease (AD) recognition, because it effectively diagnosis the pathology of the brain. Currently, computer aided diagnosis based on image analysis is an emerging tool to support AD diagnosis. In this research study, a new system is developed for enhancing the performance of AD recognition. Initially, the brain images were acquired from three online datasets and one real-time dataset such as AD Neuroimaging Initiative (ADNI), Minimal Interval Resonance Imaging in AD (MIRIAD), and Open Access Series of Imaging Studies (OASIS) and National Institute of Mental Health and Neuro Sciences (NIMHANS). Then, adaptive histogram equalization (AHE) and grey wolf optimization based clustering algorithm (GWOCA) were applied for denoising and segmenting the brain tissues; grey matter (GM), cerebro-spinal fluid (CSF), and white matter (WM) from the acquired images. After segmentation, the feature extraction was performed by utilizing dual tree complex wavelet transform (DTCWT), local ternary pattern (LTP) and Tamura features to extract the feature vectors from the segmented brain tissues. Then, ReliefF methodology was used to select the active features from the extracted feature vectors. Finally, the selected active feature values were classified into three classes [AD, normal and mild cognitive impairment (MCI)] utilizing deep neural network (DNN) classifier. From the simulation result, it is clear that the proposed framework achieved good performance in disease classification and almost showed 2.2-6% enhancement in accuracy of all four datasets.
英文关键词	Alzheimer disease recognition and classification Deep neural network Grey wolf optimization based clustering algorithm Histogram equalization ReliefF algorithm
类型	Article ; Early Access
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000666813200001
WOS关键词	MRI DATA ; MULTIMODAL CLASSIFICATION ; FEATURE-RANKING ; STRUCTURAL MRI ; DIAGNOSIS ; FUSION
WOS类目	Computer Science, Information Systems ; Computer Science, Theory & Methods
WOS研究方向	Computer Science
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/352117
作者单位	[Suresha, Halebeedu Subbaraya] Univ Mysore, PET Res Ctr, Dept ECE, Mysore, Karnataka, India; [Parthasarathy, Srirangapatna Sampathkumaran] PES Coll Engn, Dept ECE, Mandya, India
推荐引用方式 GB/T 7714	Suresha, Halebeedu Subbaraya,Parthasarathy, Srirangapatna Sampathkumaran. Detection of Alzheimer's disease using grey wolf optimization based clustering algorithm and deep neural network from magnetic resonance images[J],2021.
APA	Suresha, Halebeedu Subbaraya,&Parthasarathy, Srirangapatna Sampathkumaran.(2021).Detection of Alzheimer's disease using grey wolf optimization based clustering algorithm and deep neural network from magnetic resonance images.DISTRIBUTED AND PARALLEL DATABASES.
MLA	Suresha, Halebeedu Subbaraya,et al."Detection of Alzheimer's disease using grey wolf optimization based clustering algorithm and deep neural network from magnetic resonance images".DISTRIBUTED AND PARALLEL DATABASES (2021).