Introducing an ensemble method for the early detection of Alzheimer's disease through the analysis of PET scan images
Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that primarily affects cognitive functions such as memory, thinking, and behavior. In this disease, there is a critical phase, Mild Cognitive Impairment (MCI), that is important to be diagnosed early since some patients with p...
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Ayandegan Institute of Higher Education,
2025-03-01
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| Series: | International Journal of Research in Industrial Engineering |
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| Online Access: | https://www.riejournal.com/article_207736_6b17ab48e20a8892b834e38033fb97cf.pdf |
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| author | Arezoo Borji Taha-Hossein Hejazi Abbas Seifi |
| author_facet | Arezoo Borji Taha-Hossein Hejazi Abbas Seifi |
| author_sort | Arezoo Borji |
| collection | DOAJ |
| description | Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that primarily affects cognitive functions such as memory, thinking, and behavior. In this disease, there is a critical phase, Mild Cognitive Impairment (MCI), that is important to be diagnosed early since some patients with progressive MCI will develop the disease. When a person is in MCI, they still have significant cognitive issues, especially with memory, but they are still able to perform many daily tasks on their own. This study delves into the challenging task of classifying Alzheimer's patients into four distinct groups: Control Normal (CN), progressive Mild Cognitive Impairment (pMCI), stable Mild Cognitive Impairment (sMCI), and AD. This classification is based on a thorough examination of Positron Emission Tomography (PET) scan images obtained from the ADNI dataset, which provides a comprehensive understanding of the disease's progression. Several deep-learning and traditional machine-learning models have been used to detect AD. In this paper, three deep-learning models, namely VGG16 and AlexNet, and a custom Convolutional Neural Network (CNN) with 8-fold cross-validation, have been used for classification. Finally, an ensemble technique is used to improve the overall result of these models. The classification results show that using deep-learning models to tell the difference between MCI patients gives an overall average accuracy of 93.13% and an Area Under the Curve (AUC) of 94.4%. |
| format | Article |
| id | doaj-art-4ca2ce00f50d40769acc7186093ccb2f |
| institution | Kabale University |
| issn | 2783-1337 2717-2937 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Ayandegan Institute of Higher Education, |
| record_format | Article |
| series | International Journal of Research in Industrial Engineering |
| spelling | doaj-art-4ca2ce00f50d40769acc7186093ccb2f2025-01-30T15:10:51ZengAyandegan Institute of Higher Education,International Journal of Research in Industrial Engineering2783-13372717-29372025-03-01141658510.22105/riej.2024.452413.1434207736Introducing an ensemble method for the early detection of Alzheimer's disease through the analysis of PET scan imagesArezoo Borji0Taha-Hossein Hejazi1Abbas Seifi2Department of Industrial Engineering, College of Garmsar, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran.Department of Industrial Engineering, College of Garmsar, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran.Department of Industrial Engineering and Management Systems, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran.Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that primarily affects cognitive functions such as memory, thinking, and behavior. In this disease, there is a critical phase, Mild Cognitive Impairment (MCI), that is important to be diagnosed early since some patients with progressive MCI will develop the disease. When a person is in MCI, they still have significant cognitive issues, especially with memory, but they are still able to perform many daily tasks on their own. This study delves into the challenging task of classifying Alzheimer's patients into four distinct groups: Control Normal (CN), progressive Mild Cognitive Impairment (pMCI), stable Mild Cognitive Impairment (sMCI), and AD. This classification is based on a thorough examination of Positron Emission Tomography (PET) scan images obtained from the ADNI dataset, which provides a comprehensive understanding of the disease's progression. Several deep-learning and traditional machine-learning models have been used to detect AD. In this paper, three deep-learning models, namely VGG16 and AlexNet, and a custom Convolutional Neural Network (CNN) with 8-fold cross-validation, have been used for classification. Finally, an ensemble technique is used to improve the overall result of these models. The classification results show that using deep-learning models to tell the difference between MCI patients gives an overall average accuracy of 93.13% and an Area Under the Curve (AUC) of 94.4%.https://www.riejournal.com/article_207736_6b17ab48e20a8892b834e38033fb97cf.pdfalzheimer’s diseaseconvolutional neural networkspet scan imagesvoxel-based morphometryensemble methods |
| spellingShingle | Arezoo Borji Taha-Hossein Hejazi Abbas Seifi Introducing an ensemble method for the early detection of Alzheimer's disease through the analysis of PET scan images International Journal of Research in Industrial Engineering alzheimer’s disease convolutional neural networks pet scan images voxel-based morphometry ensemble methods |
| title | Introducing an ensemble method for the early detection of Alzheimer's disease through the analysis of PET scan images |
| title_full | Introducing an ensemble method for the early detection of Alzheimer's disease through the analysis of PET scan images |
| title_fullStr | Introducing an ensemble method for the early detection of Alzheimer's disease through the analysis of PET scan images |
| title_full_unstemmed | Introducing an ensemble method for the early detection of Alzheimer's disease through the analysis of PET scan images |
| title_short | Introducing an ensemble method for the early detection of Alzheimer's disease through the analysis of PET scan images |
| title_sort | introducing an ensemble method for the early detection of alzheimer s disease through the analysis of pet scan images |
| topic | alzheimer’s disease convolutional neural networks pet scan images voxel-based morphometry ensemble methods |
| url | https://www.riejournal.com/article_207736_6b17ab48e20a8892b834e38033fb97cf.pdf |
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