干旱区生态环境知识资源中心(Arid): Study on the effect of extreme learning machine and its variants in differentiating Alzheimer conditions from selective regions of brain MR images

Arid

DOI	10.1016/j.eswa.2022.118250
	Study on the effect of extreme learning machine and its variants in differentiating Alzheimer conditions from selective regions of brain MR images
	Shaji, Sreelakshmi; Ronickom, Jac Fredo Agastinose; Ramaniharan, Anandh Kilpattu; Swaminathan, Ramakrishnan
通讯作者	Shaji, S
来源期刊	EXPERT SYSTEMS WITH APPLICATIONS
ISSN	0957-4174
EISSN	1873-6793
出版年	2022
卷号	209
英文摘要	In this study, an attempt has been made to analyze the effect of the Extreme Learning Machine (ELM) classifier and its variants in the differentiation of Healthy Controls (HC) and Alzheimer's Disease (AD) using structural MR images. For this, sub-anatomic brain structures, namely Corpus Callosum (CC) and Lateral Ventricles (LV) are segmented and characterized using morphometric features. Significant features from these regions are subjected to ELM, online sequential ELM, and Self-adaptive Differential Evolution ELM (SaDE-ELM) classifiers for the differentiation of HC and AD. The activation functions and the number of hidden neurons in ELM classifiers are tuned by evaluating the performance using standard metrics. Results indicate that ELM and its variant classifiers are able to identify AD using morphometric features from CC and LV. ELM classifiers achieve an accuracy greater than 90% using the sigmoid activation function for both regions. Among the ELM variants, SaDE-ELM attains a maximum sensitivity of 97% and 94% using LV and CC features respectively with the lowest number of hidden neurons. The extracted features from LV demonstrate higher discriminative power than CC in AD classification. However, a specificity greater than 95% is observed in ELM for CC features. As the proposed approach is able to characterize and differentiate the morphometric alterations in CC and LV due to AD, the study seems to be clinically significant for the differentiation of HC and AD.
英文关键词	Extreme learning machine Alzheimer's disease Magnetic resonance images Lateral ventricles Corpus callosum
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000888799700001
WOS关键词	MILD COGNITIVE IMPAIRMENT ; CORPUS-CALLOSUM ; STRUCTURAL MRI ; CLASSIFICATION ; SEGMENTATION ; DISEASE ; SHAPE ; DIAGNOSIS ; OASIS
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Operations Research & Management Science
WOS研究方向	Computer Science ; Engineering ; Operations Research & Management Science
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/392571
推荐引用方式 GB/T 7714	Shaji, Sreelakshmi,Ronickom, Jac Fredo Agastinose,Ramaniharan, Anandh Kilpattu,et al. Study on the effect of extreme learning machine and its variants in differentiating Alzheimer conditions from selective regions of brain MR images[J],2022,209.
APA	Shaji, Sreelakshmi,Ronickom, Jac Fredo Agastinose,Ramaniharan, Anandh Kilpattu,&Swaminathan, Ramakrishnan.(2022).Study on the effect of extreme learning machine and its variants in differentiating Alzheimer conditions from selective regions of brain MR images.EXPERT SYSTEMS WITH APPLICATIONS,209.
MLA	Shaji, Sreelakshmi,et al."Study on the effect of extreme learning machine and its variants in differentiating Alzheimer conditions from selective regions of brain MR images".EXPERT SYSTEMS WITH APPLICATIONS 209(2022).