ADVIAN: Alzheimer's Disease VGG-Inspired Attention Network Based on Convolutional Block Attention Module and Multiple Way Data Augmentation

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.

Automated summary

The study introduces a novel method for identifying Alzheimer's disease, ADVIAN, utilizing VGG-Inspired network structure and attention mechanisms, combined with 18-way data augmentation, showing superior performance in experiments.