干旱区生态环境知识资源中心(Arid): Convolution Neural Networks and Self-Attention Learners for Alzheimer Dementia Diagnosis from Brain MRI

Arid

DOI	10.3390/s23031694
	Convolution Neural Networks and Self-Attention Learners for Alzheimer Dementia Diagnosis from Brain MRI
	Carcagni, Pierluigi; Leo, Marco; Del Coco, Marco; Distante, Cosimo; De Salve, Andrea
通讯作者	Leo, M
来源期刊	SENSORS
EISSN	1424-8220
出版年	2023
卷号	23 期号:3
英文摘要	Alzheimer's disease (AD) is the most common form of dementia. Computer-aided diagnosis (CAD) can help in the early detection of associated cognitive impairment. The aim of this work is to improve the automatic detection of dementia in MRI brain data. For this purpose, we used an established pipeline that includes the registration, slicing, and classification steps. The contribution of this research was to investigate for the first time, to our knowledge, three current and promising deep convolutional models (ResNet, DenseNet, and EfficientNet) and two transformer-based architectures (MAE and DeiT) for mapping input images to clinical diagnosis. To allow a fair comparison, the experiments were performed on two publicly available datasets (ADNI and OASIS) using multiple benchmarks obtained by changing the number of slices per subject extracted from the available 3D voxels. The experiments showed that very deep ResNet and DenseNet models performed better than the shallow ResNet and VGG versions tested in the literature. It was also found that transformer architectures, and DeiT in particular, produced the best classification results and were more robust to the noise added by increasing the number of slices. A significant improvement in accuracy (up to 7%) was achieved compared to the leading state-of-the-art approaches, paving the way for the use of CAD approaches in real-world applications.
英文关键词	assistive technology MRI medical image analysis computer-aided diagnosis masked auto-encoders deep learning vision transformers
类型	Article
语种	英语
开放获取类型	gold, Green Published
收录类别	SCI-E
WOS记录号	WOS:000932599800001
WOS关键词	DISEASE ; CLASSIFICATION
WOS类目	Chemistry, Analytical ; Engineering, Electrical & Electronic ; Instruments & Instrumentation
WOS研究方向	Chemistry ; Engineering ; Instruments & Instrumentation
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/398631
推荐引用方式 GB/T 7714	Carcagni, Pierluigi,Leo, Marco,Del Coco, Marco,et al. Convolution Neural Networks and Self-Attention Learners for Alzheimer Dementia Diagnosis from Brain MRI[J],2023,23(3).
APA	Carcagni, Pierluigi,Leo, Marco,Del Coco, Marco,Distante, Cosimo,&De Salve, Andrea.(2023).Convolution Neural Networks and Self-Attention Learners for Alzheimer Dementia Diagnosis from Brain MRI.SENSORS,23(3).
MLA	Carcagni, Pierluigi,et al."Convolution Neural Networks and Self-Attention Learners for Alzheimer Dementia Diagnosis from Brain MRI".SENSORS 23.3(2023).