干旱区生态环境知识资源中心(Arid): Alzheimer's Disease Detection via Multiscale Feature Modelling Using Improved Spatial Attention Guided Depth Separable CNN

Arid

DOI	10.1007/s44196-024-00502-y
	Alzheimer's Disease Detection via Multiscale Feature Modelling Using Improved Spatial Attention Guided Depth Separable CNN
	Tripathy, Santosh Kumar; Nayak, Rudra Kalyan; Gadupa, Kartik Shankar; Mishra, Rajnish Dinesh; Patel, Ashok Kumar; Satapathy, Santosh Kumar; Bhoi, Akash Kumar; Barsocchi, Paolo
通讯作者	Tripathy, SK
来源期刊	INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS
ISSN	1875-6891
EISSN	1875-6883
出版年	2024
卷号	17 期号:1
英文摘要	Early detection of Alzheimer's disease (AD) is critical due to its rising prevalence. AI-aided AD diagnosis has grown for decades. Most of these systems use deep learning using CNN. However, a few concerns must be addressed to identify AD: a. there is a lack of attention paid to spatial features; b. there is a lack of scale-invariant feature modelling; and c. the convolutional spatial attention block (C-SAB) mechanism is available in the literature, but it exploits limited feature sets from its input features to obtain a spatial attention map, which needs to be enhanced. The suggested model addresses these issues in two ways: through a backbone of multilayers of depth-separable CNN. Firstly, we propose an improved spatial convolution attention block (I-SAB) to generate an enhanced spatial attention map for the multilayer features of the backbone. The I-SAB, a modified version of the C-SAB, generates a spatial attention map by combining multiple cues from input feature maps. Such a map is forwarded to a multilayer of depth-separable CNN for further feature extraction and employs a skip connection to produce an enhanced spatial attention map. Second, we combine multilayer spatial attention features to make scale-invariant spatial attention features that can fix scale issues in MRI images. We demonstrate extensive experimentation and ablation studies using two open-source datasets, OASIS and AD-Dataset. The recommended model outperforms existing best practices with 99.75% and 96.20% accuracy on OASIS and AD-Dataset. This paper also performed a domain adaptation test on the OASIS dataset, which obtained 83.25% accuracy.
英文关键词	Alzheimer's disease Deep learning Depth separable CNN Spatial attention Improved spatial attention
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:001215433400001
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications
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/2XILL650/404221
推荐引用方式 GB/T 7714	Tripathy, Santosh Kumar,Nayak, Rudra Kalyan,Gadupa, Kartik Shankar,et al. Alzheimer's Disease Detection via Multiscale Feature Modelling Using Improved Spatial Attention Guided Depth Separable CNN[J],2024,17(1).
APA	Tripathy, Santosh Kumar.,Nayak, Rudra Kalyan.,Gadupa, Kartik Shankar.,Mishra, Rajnish Dinesh.,Patel, Ashok Kumar.,...&Barsocchi, Paolo.(2024).Alzheimer's Disease Detection via Multiscale Feature Modelling Using Improved Spatial Attention Guided Depth Separable CNN.INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS,17(1).
MLA	Tripathy, Santosh Kumar,et al."Alzheimer's Disease Detection via Multiscale Feature Modelling Using Improved Spatial Attention Guided Depth Separable CNN".INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 17.1(2024).