New Convolutional Neural Network and Graph Convolutional Network-Based Architecture for AI Applications in Alzheimer’s Disease and Dementia-Stage Classification
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, compute...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | AI |
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| Online Access: | https://www.mdpi.com/2673-2688/5/1/17 |
| _version_ | 1797242362368360448 |
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| author | Md Easin Hasan Amy Wagler |
| author_facet | Md Easin Hasan Amy Wagler |
| author_sort | Md Easin Hasan |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T18:38:01Z |
| format | Article |
| id | doaj.art-b6f9d5fcfaa74725b1406deaa2aa5953 |
| institution | Directory Open Access Journal |
| issn | 2673-2688 |
| language | English |
| last_indexed | 2024-04-24T18:38:01Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | AI |
| spelling | doaj.art-b6f9d5fcfaa74725b1406deaa2aa59532024-03-27T13:17:11ZengMDPI AGAI2673-26882024-02-015134236310.3390/ai5010017New Convolutional Neural Network and Graph Convolutional Network-Based Architecture for AI Applications in Alzheimer’s Disease and Dementia-Stage ClassificationMd Easin Hasan0Amy Wagler1Department of Mathematical Sciences, The University of Texas at El Paso, 500 W. University Ave., El Paso, TX 79968, USADepartment of Public Health, The University of Texas at El Paso, 500 W. University Ave., El Paso, TX 79968, USANeuroimaging 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.https://www.mdpi.com/2673-2688/5/1/17Alzheimer’s diseaseimage classificationtransfer learningconvolutional neural networksgraph convolutional networks |
| spellingShingle | Md Easin Hasan Amy Wagler New Convolutional Neural Network and Graph Convolutional Network-Based Architecture for AI Applications in Alzheimer’s Disease and Dementia-Stage Classification AI Alzheimer’s disease image classification transfer learning convolutional neural networks graph convolutional networks |
| title | New Convolutional Neural Network and Graph Convolutional Network-Based Architecture for AI Applications in Alzheimer’s Disease and Dementia-Stage Classification |
| title_full | New Convolutional Neural Network and Graph Convolutional Network-Based Architecture for AI Applications in Alzheimer’s Disease and Dementia-Stage Classification |
| title_fullStr | New Convolutional Neural Network and Graph Convolutional Network-Based Architecture for AI Applications in Alzheimer’s Disease and Dementia-Stage Classification |
| title_full_unstemmed | New Convolutional Neural Network and Graph Convolutional Network-Based Architecture for AI Applications in Alzheimer’s Disease and Dementia-Stage Classification |
| title_short | New Convolutional Neural Network and Graph Convolutional Network-Based Architecture for AI Applications in Alzheimer’s Disease and Dementia-Stage Classification |
| title_sort | new convolutional neural network and graph convolutional network based architecture for ai applications in alzheimer s disease and dementia stage classification |
| topic | Alzheimer’s disease image classification transfer learning convolutional neural networks graph convolutional networks |
| url | https://www.mdpi.com/2673-2688/5/1/17 |
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