干旱区生态环境知识资源中心(Arid): 3D brain image-based Alzheimer's disease detection techniques using fish swarm optimizer's deep convolution Siamese neural network

Arid

DOI	10.1111/exsy.12963
	3D brain image-based Alzheimer's disease detection techniques using fish swarm optimizer's deep convolution Siamese neural network
	Sampath, Rajaram; Baskar, Manickam
通讯作者	Baskar, M
来源期刊	EXPERT SYSTEMS
ISSN	0266-4720
EISSN	1468-0394
出版年	2022
英文摘要	Alzheimer's disease (AD), a chronic syndrome that impacts the brain, is the most prevalent form of dementia. Dementia is a brain disease that severely affects an individual's ability to perform daily activities. It starts slowly affects the brain and creates a loss of memory, language, problem-solving and other thinking abilities. Hence, early detection is essential to avoid the severity of this illness. Neuroimaging techniques are widely recommended diagnosing approaches by medicos for early AD detection. However, detecting AD using imaging is a challenging and time-consuming task for human expertise. Many machine learning techniques already exist in automatic AD stages detection, but these techniques are failed to handle main issues in AD detection systems such, as preserving and identifying precise biomarker regions certainty handling and; in this research, a new convolution-based AD stages detection framework is introduced to resolve the earlier detection system's challenges and issues. The first two convolution layers contain resizing, adaptive filtering, and adaptive histogram equalization techniques to enhance the image quality, preserving biomarker features. The third layer contains the Voxel-based Morphometry (VBM) technique to segment the exact biomarker regions of AD stages from brain MRI images. The segmented biomarker feature is extracted and selected in the fourth and fifth layers to identify exact significant biomarker features to reduce the overfitting problem during the model training. Finally, the new food source direction investigation feature of the fish swarm optimizer (FSO) is incorporated in the deep Siamese neural network (DSNN) classification phase, which reduces the uncertainty issue during model training. The efficiency is evaluated using ADNI, AIBL, and OASIS database MRI images with various accuracy metrics. The evolution results show that the new framework is obtained a higher accuracy rate of 99.89% in AD stages detection than the comparison classifiers.
英文关键词	3D MRI brain image Alzheimer's disease biomarker features deep convolution Siamese neural network feature extraction fish swarm optimizer image enhancement segmentation
类型	Article ; Early Access
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000762565000001
WOS关键词	PREDICTION
WOS类目	Computer Science, Artificial Intelligence ; 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/2XILL650/377388
推荐引用方式 GB/T 7714	Sampath, Rajaram,Baskar, Manickam. 3D brain image-based Alzheimer's disease detection techniques using fish swarm optimizer's deep convolution Siamese neural network[J],2022.
APA	Sampath, Rajaram,&Baskar, Manickam.(2022).3D brain image-based Alzheimer's disease detection techniques using fish swarm optimizer's deep convolution Siamese neural network.EXPERT SYSTEMS.
MLA	Sampath, Rajaram,et al."3D brain image-based Alzheimer's disease detection techniques using fish swarm optimizer's deep convolution Siamese neural network".EXPERT SYSTEMS (2022).