RETRACTED: 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 m...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-06-01
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| Series: | Frontiers in Aging Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnagi.2021.687456/full |
| _version_ | 1797205587570720768 |
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| author | Shui-Hua Wang Shui-Hua Wang Qinghua Zhou Ming Yang Yu-Dong Zhang Yu-Dong Zhang |
| author_facet | Shui-Hua Wang Shui-Hua Wang Qinghua Zhou Ming Yang Yu-Dong Zhang Yu-Dong Zhang |
| author_sort | Shui-Hua Wang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T18:55:07Z |
| format | Article |
| id | doaj.art-a00177d053104730804d4ff9a71e20aa |
| institution | Directory Open Access Journal |
| issn | 1663-4365 |
| language | English |
| last_indexed | 2024-04-24T08:53:30Z |
| publishDate | 2021-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Aging Neuroscience |
| spelling | doaj.art-a00177d053104730804d4ff9a71e20aa2024-04-16T09:13:09ZengFrontiers Media S.A.Frontiers in Aging Neuroscience1663-43652021-06-011310.3389/fnagi.2021.687456687456RETRACTED: ADVIAN: Alzheimer's Disease VGG-Inspired Attention Network Based on Convolutional Block Attention Module and Multiple Way Data AugmentationShui-Hua Wang0Shui-Hua Wang1Qinghua Zhou2Ming Yang3Yu-Dong Zhang4Yu-Dong Zhang5Key Laboratory of Child Development and Learning Science (Southeast University), Ministry of Education, Nanjing, ChinaSchool of Mathematics and Actuarial Science, University of Leicester, Leicester, United KingdomSchool of Informatics, University of Leicester, Leicester, United KingdomDepartment of Radiology, Children's Hospital of Nanjing Medical University, Nanjing, ChinaKey Laboratory of Child Development and Learning Science (Southeast University), Ministry of Education, Nanjing, ChinaSchool of Informatics, University of Leicester, Leicester, United KingdomAim: 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.https://www.frontiersin.org/articles/10.3389/fnagi.2021.687456/fullAlzheimer‘s diseaseconvolutional block attention moduleVGGtransfer learningdeep learningattention network |
| spellingShingle | Shui-Hua Wang Shui-Hua Wang Qinghua Zhou Ming Yang Yu-Dong Zhang Yu-Dong Zhang RETRACTED: ADVIAN: Alzheimer's Disease VGG-Inspired Attention Network Based on Convolutional Block Attention Module and Multiple Way Data Augmentation Frontiers in Aging Neuroscience Alzheimer‘s disease convolutional block attention module VGG transfer learning deep learning attention network |
| title | RETRACTED: ADVIAN: Alzheimer's Disease VGG-Inspired Attention Network Based on Convolutional Block Attention Module and Multiple Way Data Augmentation |
| title_full | RETRACTED: ADVIAN: Alzheimer's Disease VGG-Inspired Attention Network Based on Convolutional Block Attention Module and Multiple Way Data Augmentation |
| title_fullStr | RETRACTED: ADVIAN: Alzheimer's Disease VGG-Inspired Attention Network Based on Convolutional Block Attention Module and Multiple Way Data Augmentation |
| title_full_unstemmed | RETRACTED: ADVIAN: Alzheimer's Disease VGG-Inspired Attention Network Based on Convolutional Block Attention Module and Multiple Way Data Augmentation |
| title_short | RETRACTED: ADVIAN: Alzheimer's Disease VGG-Inspired Attention Network Based on Convolutional Block Attention Module and Multiple Way Data Augmentation |
| title_sort | retracted advian alzheimer s disease vgg inspired attention network based on convolutional block attention module and multiple way data augmentation |
| topic | Alzheimer‘s disease convolutional block attention module VGG transfer learning deep learning attention network |
| url | https://www.frontiersin.org/articles/10.3389/fnagi.2021.687456/full |
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