AUTHOR=Wang Shui-Hua , Zhou Qinghua , Yang Ming , Zhang Yu-Dong 

TITLE=RETRACTED: 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=Volume 13 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/aging-neuroscience/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.