干旱区生态环境知识资源中心(Arid): Multimodal Neuroimaging Fusion for Alzheimer's Disease: An Image Colorization Approach With Mobile Vision Transformer

Arid

DOI	10.1002/ima.23158
	Multimodal Neuroimaging Fusion for Alzheimer's Disease: An Image Colorization Approach With Mobile Vision Transformer
	Odusami, Modupe; Damasevicius, Robertas; Milieskaite-Belousoviene, Egle; Maskeliunas, Rytis
通讯作者	Maskeliunas, R
来源期刊	INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY
ISSN	0899-9457
EISSN	1098-1098
出版年	2024
卷号	34 期号:5
英文摘要	Multimodal neuroimaging, combining data from different sources, has shown promise in the classification of the Alzheimer's disease (AD) stage. Existing multimodal neuroimaging fusion methods exhibit certain limitations, which require advancements to enhance their objective performance, sensitivity, and specificity for AD classification. This study uses the use of a Pareto-optimal cosine color map to enhance classification performance and visual clarity of fused images. A mobile vision transformer (ViT) model, incorporating the swish activation function, is introduced for effective feature extraction and classification. Fused images from the Alzheimer's Disease Neuroimaging Initiative (ADNI), the Whole Brain Atlas (AANLIB), and Open Access Series of Imaging Studies (OASIS) datasets, obtained through optimized transposed convolution, are utilized for model training, while evaluation is achieved using images that have not been fused from the same databases. The proposed model demonstrates high accuracy in AD classification across different datasets, achieving 98.76% accuracy for Early Mild Cognitive Impairment (EMCI) versus LMCI, 98.65% for Late Mild Cognitive Impairment (LMCI) versus AD, 98.60% for EMCI versus AD, and 99.25% for AD versus Cognitive Normal (CN) in the ADNI dataset. Similarly, on OASIS and AANLIB, the precision of the AD versus CN classification is 99.50% and 96.00%, respectively. Evaluation metrics showcase the model's precision, recall, and F1 score for various binary classifications, emphasizing its robust performance.
英文关键词	Alzheimer's disease MRI colorization vision transformer
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001298112800001
WOS关键词	MRI ; PET
WOS类目	Engineering, Electrical & Electronic ; Optics ; Imaging Science & Photographic Technology
WOS研究方向	Engineering ; Optics ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/404251
推荐引用方式 GB/T 7714	Odusami, Modupe,Damasevicius, Robertas,Milieskaite-Belousoviene, Egle,et al. Multimodal Neuroimaging Fusion for Alzheimer's Disease: An Image Colorization Approach With Mobile Vision Transformer[J],2024,34(5).
APA	Odusami, Modupe,Damasevicius, Robertas,Milieskaite-Belousoviene, Egle,&Maskeliunas, Rytis.(2024).Multimodal Neuroimaging Fusion for Alzheimer's Disease: An Image Colorization Approach With Mobile Vision Transformer.INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY,34(5).
MLA	Odusami, Modupe,et al."Multimodal Neuroimaging Fusion for Alzheimer's Disease: An Image Colorization Approach With Mobile Vision Transformer".INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 34.5(2024).