干旱区生态环境知识资源中心(Arid): Dementia classification from magnetic resonance images by machine learning

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DOI	10.1007/s00521-023-09163-y
	Dementia classification from magnetic resonance images by machine learning
	Waldo-Benitez, Georgina; Padierna, Luis Carlos; Ceron, Pablo; Sosa, Modesto A.
通讯作者	Padierna, LC
来源期刊	NEURAL COMPUTING & APPLICATIONS
ISSN	0941-0643
EISSN	1433-3058
出版年	2024
卷号	36 期号:6 页码:2653-2664
英文摘要	Dementia is a threatening condition that affects communication, thinking, and memory skills, being Alzheimer its most common type. The early detection of this disease allows for better care of the patient. Recently, Machine Learning (ML) methods have been developed to support the finding and forecast of Alzheimer's disease through the analysis of Magnetic Resonance Images (MRI). Existing ML methods present some limitations: (i) require an expert to extract relevant features from MRI, (ii) depend on multistep image preprocessing, or (iii) need complex architectures and several images to train them. To surpass these limitations, in the present work, we analyze different Convolutional Neural Networks (CNNs) for Alzheimer's classification, formulated to learn from a set of representative MRI sagittal images available in the Open Access Series of Imaging Studies (OASIS-2, 72 non-demented and 64 demented subjects, with ages from 60 to 96 years) and the Alzheimer's Disease Neuroimaging Initiative (ADNI, 200 early Alzheimer and 200 control patients, with ages from 55 to 90 years) datasets. All CNNs were compared with state-of-the-art ML methods, being the VGG-16 variant the best performed architecture with an average validation accuracy of 56% +/- 4%, evaluated with a bootstrapping strategy to measure the variability on independent runs. This result confirms the best performance reported so far (< 60%) with different ML methods. The low accuracy evidences the hardness of the problem and contrasts with the higher accuracy levels (up to 97%) reached with preprocessed and well-characterized MRI axial images from the OASIS-1 or ADNI-2 datasets. Thus, opening an interesting discussion about what MRI plane should be considered when training CNNs for Alzheimer's classification, and leaving a wide room for improvement on the performance of CNNs trained with sagittal MRI images. The resulting model implemented in software and experimental data are publicly available.
英文关键词	Convolutional neural network Alzheimer classification Machine learning Brain MRI OASIS-2
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001147954900015
WOS关键词	MRI
WOS类目	Computer Science, Artificial Intelligence
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/2XILL650/404976
推荐引用方式 GB/T 7714	Waldo-Benitez, Georgina,Padierna, Luis Carlos,Ceron, Pablo,et al. Dementia classification from magnetic resonance images by machine learning[J],2024,36(6):2653-2664.
APA	Waldo-Benitez, Georgina,Padierna, Luis Carlos,Ceron, Pablo,&Sosa, Modesto A..(2024).Dementia classification from magnetic resonance images by machine learning.NEURAL COMPUTING & APPLICATIONS,36(6),2653-2664.
MLA	Waldo-Benitez, Georgina,et al."Dementia classification from magnetic resonance images by machine learning".NEURAL COMPUTING & APPLICATIONS 36.6(2024):2653-2664.