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Cited by 31Dementia and Cognitive Impairment ResearchAdvanced Neural Network ApplicationsFunctional Brain Connectivity Studies

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New Convolutional Neural Network and Graph Convolutional Network-Based Architecture for AI Applications in Alzheimer’s Disease and Dementia-Stage Classification

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Hasan, Md Easin, Wagler, Amy·2024·AI
DOI 10.3390/ai5010017
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Abstract

Neuroimaging experts in biotech industries can benefit from using cutting-edge artificial intelligence techniques for Alzheimer’s disease (AD)- and dementia-stage prediction, even though it is difficult to anticipate the precise stage of dementia and AD. Therefore, we propose a cutting-edge, computer-assisted method based on an advanced deep learning algorithm to differentiate between people with varying degrees of dementia, including healthy, very mild dementia, mild dementia, and moderate dementia classes. In this paper, four separate models were developed for classifying different dementia stages: convolutional neural networks (CNNs) built from scratch, pre-trained VGG16 with additional convolutional layers, graph convolutional networks (GCNs), and CNN-GCN models. The CNNs were implemented, and then the flattened layer output was fed to the GCN classifier, resulting in the proposed CNN-GCN architecture. A total of 6400 whole-brain magnetic resonance imaging scans were obtained from the Alzheimer’s Disease Neuroimaging Initiative database to train and evaluate the proposed methods. We applied the 5-fold cross-validation (CV) technique for all the models. We presented the results from the best fold out of the five folds in assessing the performance of the models developed in this study. Hence, for the best fold of the 5-fold CV, the above-mentioned models achieved an overall accuracy of 43.83%, 71.17%, 99.06%, and 100%, respectively. The CNN-GCN model, in particular, demonstrates excellent performance in classifying different stages of dementia. Understanding the stages of dementia can assist biotech industry researchers in uncovering molecular markers and pathways connected with each stage.

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Figure 1 . Figure 1. The architecture of the proposed CNN-GCN model.
Figure 1 . Figure 1. The architecture of the proposed CNN-GCN model.CC-BY · From the PDF
Figure 2 . Figure 2. Moderate dementia image: (a) original and (b) Grad-CAM image.
Figure 2 . Figure 2. Moderate dementia image: (a) original and (b) Grad-CAM image.CC-BY · From the PDF
Figure 3 . Figure 3. Total number of images by each category.
Figure 3 . Figure 3. Total number of images by each category.CC-BY · From the PDF
Figure 4 . Figure 4. Epoch versus cost for the CNN model.
Figure 4 . Figure 4. Epoch versus cost for the CNN model.CC-BY · From the PDF
Figure 5 . Figure 5. Epoch versus accuracy for the CNN model.
Figure 5 . Figure 5. Epoch versus accuracy for the CNN model.CC-BY · From the PDF
Figure 6 . Figure 6. Confusion matrix for the CNN model.
Figure 6 . Figure 6. Confusion matrix for the CNN model.CC-BY · From the PDF
Figure 7 . Figure 7. Epoch vs. cost for the VGG16 with additional convolutional layers model.
Figure 7 . Figure 7. Epoch vs. cost for the VGG16 with additional convolutional layers model.CC-BY · From the PDF
Figure 8 . Figure 8. Epoch vs. accuracy for the VGG16 with additional convolutional layers model.
Figure 8 . Figure 8. Epoch vs. accuracy for the VGG16 with additional convolutional layers model.CC-BY · From the PDF
Figure 9 . Figure 9. Confusion matrix for the VGG16 with additional convolutional layers model.
Figure 9 . Figure 9. Confusion matrix for the VGG16 with additional convolutional layers model.CC-BY · From the PDF
Figure 10 . Figure 10. Epoch versus cost for the GCN model.
Figure 10 . Figure 10. Epoch versus cost for the GCN model.CC-BY · From the PDF
Figure 11 . Figure 11. Epoch versus accuracy for the GCN model.
Figure 11 . Figure 11. Epoch versus accuracy for the GCN model.CC-BY · From the PDF
Figure 12 . Figure 12. Confusion matrix for the GCN model.
Figure 12 . Figure 12. Confusion matrix for the GCN model.CC-BY · From the PDF
Figure 13 . Figure 13. Epoch versus cost for the CNN-GCN model.
Figure 13 . Figure 13. Epoch versus cost for the CNN-GCN model.CC-BY · From the PDF
Figure 14 . Figure 14. Epoch versus accuracy for the CNN-GCN model.
Figure 14 . Figure 14. Epoch versus accuracy for the CNN-GCN model.CC-BY · From the PDF
Figure 15 Figure15displays the confusion matrices produced by the CNN-GCN model for the categorization of the healthy, very mild, mild, and moderate classes. The number of correctly predicted healthy cases is 641, the number of very mild cases is 448, the number of mild cases is 179, and the number of moderate cases is 12. All category images are predicted correctly using the CNN-GCN model.
Figure 15 Figure15displays the confusion matrices produced by the CNN-GCN model for the categorization of the healthy, very mild, mild, and moderate classes. The number of correctly predicted healthy cases is 641, the number of very mild cases is 448, the number of mild cases is 179, and the number of moderate cases is 12. All category images are predicted correctly using the CNN-GCN model.CC-BY · From the PDF
Figure 16 . Figure 16. Confusion matrix for the CNN-GCN model for classifying AD, MCI, and CN.
Figure 16 . Figure 16. Confusion matrix for the CNN-GCN model for classifying AD, MCI, and CN.CC-BY · From the PDF

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Citations

Cites

Research works printed but not linked: 24

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Cited by

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Metadata

Authors
Hasan, M. E., & Wagler, A.
Affiliations
  • Department of Mathematical Sciences, The University of Texas at El Paso, 500 W. University Ave., El Paso, TX 79968, USA
  • The University of Texas at El Paso (US)
ORCID
Venue
AI
Publisher
MDPI AG
Published
2024-02-01
Volume
5
Issue
1
Pages
342-363
ISSN
2673-2688 (ISSN-L)
DOI
10.3390/ai5010017
OpenAlex
W4391451187
Cited by
31
References
1
Citations by year
2026: 7 · 2025: 15 · 2024: 9
FWCI
7.33
Type
journal article
Language
en
Open access
Licence
Locations
  • AI (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Topics
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Keywords
Alzheimer's disease, dementia staging, dementia classification, graph convolutional networks, convolutional neural networks, whole-brain MRI, ADNI, neuroimaging, deep learning classification

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