Knowledge Resource Center for Ecological Environment in Arid Area
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
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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). |
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