干旱区生态环境知识资源中心(Arid): Alzheimer's disease stage recognition from MRI and PET imaging data using Pareto-optimal quantum dynamic optimization

Arid

DOI	10.1016/j.heliyon.2024.e34402
	Alzheimer's disease stage recognition from MRI and PET imaging data using Pareto-optimal quantum dynamic optimization
	Odusami, Modupe; Damasevicius, Robertas; Milieskaite-Belousoviene, Egle; Maskeliu over bar nas, Rytis
通讯作者	bar Nas, RMO
来源期刊	HELIYON
EISSN	2405-8440
出版年	2024
卷号	10 期号:15
英文摘要	The threat posed by Alzheimer's disease (AD) to human health has grown significantly. However, the precise diagnosis and classification of AD stages remain a challenge. Neuroimaging methods such as structural magnetic resonance imaging (sMRI) and fluorodeoxyglucose positron emission tomography (FDG-PET) have been used to diagnose and categorize AD. However, feature selection approaches that are frequently used to extract additional data from multimodal imaging are prone to errors. This paper suggests using a static pulse-coupled neural network and a Laplacian pyramid to combine sMRI and FDG-PET data. After that, the fused images are used to train the Mobile Vision Transformer (MViT), optimized with Pareto-Optimal Quantum Dynamic Optimization for Neural Architecture Search, while the fused images are augmented to avoid overfitting and then classify unfused MRI and FDG-PET images obtained from the AD Neuroimaging Initiative (ADNI) and Open Access Series of Imaging Studies (OASIS) datasets into various stages of AD. The architectural hyperparameters of MViT are optimized using Quantum Dynamic Optimization, which ensures a Pareto-optimal solution. The Peak Signal-to-Noise Ratio (PSNR), the Mean Squared Error (MSE), and the Structured Similarity Indexing Method (SSIM) are used to measure the quality of the fused image. We found that the fused image was consistent in all metrics, having 0.64 SIMM, 35.60 PSNR, and 0.21 MSE for the FDG-PET image. In the classification of AD vs. cognitive normal (CN), AD vs. mild cognitive impairment (MCI), and CN vs. MCI, the precision of the proposed method is 94.73%, 92.98% and 89.36%, respectively. The sensitivity is 90. 70%, 90. 70%, and 90. 91% while the specificity is 100%, 100%, and 85. 71%, respectively, in the ADNI MRI test data.
英文关键词	Alzheimer's disease Pareto optimization Deep learning Classification Image fusion Multimodal
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001279174900001
WOS关键词	COUPLED NEURAL-NETWORK ; FUSION ; CLASSIFICATION
WOS类目	Multidisciplinary Sciences
WOS研究方向	Science & Technology - Other Topics
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/404043
推荐引用方式 GB/T 7714	Odusami, Modupe,Damasevicius, Robertas,Milieskaite-Belousoviene, Egle,et al. Alzheimer's disease stage recognition from MRI and PET imaging data using Pareto-optimal quantum dynamic optimization[J],2024,10(15).
APA	Odusami, Modupe,Damasevicius, Robertas,Milieskaite-Belousoviene, Egle,&Maskeliu over bar nas, Rytis.(2024).Alzheimer's disease stage recognition from MRI and PET imaging data using Pareto-optimal quantum dynamic optimization.HELIYON,10(15).
MLA	Odusami, Modupe,et al."Alzheimer's disease stage recognition from MRI and PET imaging data using Pareto-optimal quantum dynamic optimization".HELIYON 10.15(2024).