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@Article{brainsci12030319,
AUTHOR = {Jiang, Mingfeng and Yan, Bin and Li, Yang and Zhang, Jucheng and Li, Tieqiang and Ke, Wei},
TITLE = {Image Classification of Alzheimer&rsquo;s Disease Based on External-Attention Mechanism and Fully Convolutional Network},
JOURNAL = {Brain Sciences},
VOLUME = {12},
YEAR = {2022},
NUMBER = {3},
ARTICLE-NUMBER = {319},
URL = {https://www.mdpi.com/2076-3425/12/3/319},
PubMedID = {35326275},
ISSN = {2076-3425},
ABSTRACT = {Automatic and accurate classification of Alzheimer&rsquo;s disease is a challenging and promising task. Fully Convolutional Network (FCN) can classify images at the pixel level. Adding an attention mechanism to the Fully Convolutional Network can effectively improve the classification performance of the model. However, the self-attention mechanism ignores the potential correlation between different samples. Aiming at this problem, we propose a new method for image classification of Alzheimer&rsquo;s disease based on the external-attention mechanism. The external-attention module is added after the fourth convolutional block of the fully convolutional network model. At the same time, the double normalization method of Softmax and L1 norm is introduced to obtain a better classification performance and richer feature information of the disease probability map. The activation function Softmax can increase the degree of fitting of the neural network to the training set, which transforms linearity into nonlinearity, thereby increasing the flexibility of the neural network. The L1 norm can avoid the attention map being affected by especially large (especially small) eigenvalues. The experiments in this paper use 550 three-dimensional MRI images and use five-fold cross-validation. The experimental results show that the proposed image classification method for Alzheimer&rsquo;s disease, combining the external-attention mechanism with double normalization, can effectively improve the classification performance of the model. With this method, the accuracy of the MLP-A model is 92.36%, the accuracy of the MLP-B model is 98.55%, and the accuracy of the fusion model MLP-C is 98.73%. The classification performance of the model is higher than similar models without adding any attention mechanism, and it is better than other comparison methods.},
DOI = {10.3390/brainsci12030319}
}
@ARTICLE{10.3389/fnagi.2021.687456,
  
AUTHOR={Wang, Shui-Hua and Zhou, Qinghua and Yang, Ming and Zhang, Yu-Dong},   
	 
TITLE={ADVIAN: Alzheimer's Disease VGG-Inspired Attention Network Based on Convolutional Block Attention Module and Multiple Way Data Augmentation},      
	
JOURNAL={Frontiers in Aging Neuroscience},      
	
VOLUME={13},           
	
YEAR={2021},      
	  
URL={https://www.frontiersin.org/articles/10.3389/fnagi.2021.687456},       
	
DOI={10.3389/fnagi.2021.687456},      
	
ISSN={1663-4365},   
   
ABSTRACT={Aim: Alzheimer's disease is a neurodegenerative disease that causes 60–70% of all cases of dementia. This study is to provide a novel method that can identify AD more accurately.Methods: We first propose a VGG-inspired network (VIN) as the backbone network and investigate the use of attention mechanisms. We proposed an Alzheimer's Disease VGG-Inspired Attention Network (ADVIAN), where we integrate convolutional block attention modules on a VIN backbone. Also, 18-way data augmentation is proposed to avoid overfitting. Ten runs of 10-fold cross-validation are carried out to report the unbiased performance.Results: The sensitivity and specificity reach 97.65 ± 1.36 and 97.86 ± 1.55, respectively. Its precision and accuracy are 97.87 ± 1.53 and 97.76 ± 1.13, respectively. The F1 score, MCC, and FMI are obtained as 97.75 ± 1.13, 95.53 ± 2.27, and 97.76 ± 1.13, respectively. The AUC is 0.9852.Conclusion: The proposed ADVIAN gives better results than 11 state-of-the-art methods. Besides, experimental results demonstrate the effectiveness of 18-way data augmentation.}
}
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