Transforming Alzheimer’s Disease Diagnosis: Implementing Vision Transformer (ViT) for MRI Images Classification
Alzheimer’s disease (AD) is a progressive neurological disorder and the leading cause of dementia, accounting for 60-80% of cases. Early detection of AD is crucial for timely intervention, as the disease significantly impacts cognitive functions and daily activities. Diagnosing AD in clinical setti...
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| Format: | Article |
| Language: | English |
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UUM Press
2025-01-01
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| Series: | Journal of ICT |
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| Online Access: | https://e-journal.uum.edu.my/index.php/jict/article/view/25840 |
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| author | Dian Kurniasari Muhammad Dwi Pratama Akmal Junaidi Ahmad Faisol |
| author_facet | Dian Kurniasari Muhammad Dwi Pratama Akmal Junaidi Ahmad Faisol |
| author_sort | Dian Kurniasari |
| collection | DOAJ |
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Alzheimer’s disease (AD) is a progressive neurological disorder and the leading cause of dementia, accounting for 60-80% of cases. Early detection of AD is crucial for timely intervention, as the disease significantly impacts cognitive functions and daily activities. Diagnosing AD in clinical settings remains challenging due to subtle early symptoms, diverse presentations, lengthy diagnostic processes, and inconsistent criteria that heavily rely on medical expertise. Accurate and timely diagnosis during the early stages is essential for effective treatment and intervention. Modern imaging techniques, such as Magnetic Resonance Imaging (MRI), have become essential in diagnosing Alzheimer’s by providing detailed insights into structural brain changes. This study explores the application of the Vision Transformer (ViT) model for classifying MRI images of Alzheimer’s patients, focusing on enhancing accuracy and reliability through data augmentation during pre-processing. A dataset of 8,000 MRI images, categorised into four groups—non-demented, very mild demented, mild demented, and moderate demented—was used to evaluate the ViT model. The experiment achieved promising results, with an accuracy of 98.19%, sensitivity of 96.34%, specificity of 98.80%, and an F1-score of 96.37%. These findings underscore the model’s effectiveness in distinguishing between affected and unaffected individuals, minimising misdiagnosis and enabling timely clinical interventions. However, some challenges remain, particularly in the classification between “Non-Demented” and “Very Mild Demented” cases. Future research should focus on enhancing data augmentation techniques and increasing data diversity in these categories to improve the model’s performance further. The ViT model holds great potential for advancing Alzheimer’s diagnosis, offering a valuable tool for early detection and intervention in clinical settings.
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| format | Article |
| id | doaj-art-777b2df536524c7685656feb23bd2ff1 |
| institution | Kabale University |
| issn | 1675-414X 2180-3862 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | UUM Press |
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| series | Journal of ICT |
| spelling | doaj-art-777b2df536524c7685656feb23bd2ff12025-02-09T01:42:45ZengUUM PressJournal of ICT1675-414X2180-38622025-01-0124110.32890/jict2025.24.1.6Transforming Alzheimer’s Disease Diagnosis: Implementing Vision Transformer (ViT) for MRI Images ClassificationDian Kurniasari0Muhammad Dwi Pratama1Akmal Junaidi2Ahmad Faisol3Department of Mathematics, Universitas Lampung, IndonesiaDepartment of Mathematics, Universitas Lampung, IndonesiaDepartment of Computer Science, Universitas Lampung, IndonesiaDepartment of Mathematics, Universitas Lampung, Indonesia Alzheimer’s disease (AD) is a progressive neurological disorder and the leading cause of dementia, accounting for 60-80% of cases. Early detection of AD is crucial for timely intervention, as the disease significantly impacts cognitive functions and daily activities. Diagnosing AD in clinical settings remains challenging due to subtle early symptoms, diverse presentations, lengthy diagnostic processes, and inconsistent criteria that heavily rely on medical expertise. Accurate and timely diagnosis during the early stages is essential for effective treatment and intervention. Modern imaging techniques, such as Magnetic Resonance Imaging (MRI), have become essential in diagnosing Alzheimer’s by providing detailed insights into structural brain changes. This study explores the application of the Vision Transformer (ViT) model for classifying MRI images of Alzheimer’s patients, focusing on enhancing accuracy and reliability through data augmentation during pre-processing. A dataset of 8,000 MRI images, categorised into four groups—non-demented, very mild demented, mild demented, and moderate demented—was used to evaluate the ViT model. The experiment achieved promising results, with an accuracy of 98.19%, sensitivity of 96.34%, specificity of 98.80%, and an F1-score of 96.37%. These findings underscore the model’s effectiveness in distinguishing between affected and unaffected individuals, minimising misdiagnosis and enabling timely clinical interventions. However, some challenges remain, particularly in the classification between “Non-Demented” and “Very Mild Demented” cases. Future research should focus on enhancing data augmentation techniques and increasing data diversity in these categories to improve the model’s performance further. The ViT model holds great potential for advancing Alzheimer’s diagnosis, offering a valuable tool for early detection and intervention in clinical settings. https://e-journal.uum.edu.my/index.php/jict/article/view/25840Alzheimer’sdeep learningimage classificationMRIvision transformer |
| spellingShingle | Dian Kurniasari Muhammad Dwi Pratama Akmal Junaidi Ahmad Faisol Transforming Alzheimer’s Disease Diagnosis: Implementing Vision Transformer (ViT) for MRI Images Classification Journal of ICT Alzheimer’s deep learning image classification MRI vision transformer |
| title | Transforming Alzheimer’s Disease Diagnosis: Implementing Vision Transformer (ViT) for MRI Images Classification |
| title_full | Transforming Alzheimer’s Disease Diagnosis: Implementing Vision Transformer (ViT) for MRI Images Classification |
| title_fullStr | Transforming Alzheimer’s Disease Diagnosis: Implementing Vision Transformer (ViT) for MRI Images Classification |
| title_full_unstemmed | Transforming Alzheimer’s Disease Diagnosis: Implementing Vision Transformer (ViT) for MRI Images Classification |
| title_short | Transforming Alzheimer’s Disease Diagnosis: Implementing Vision Transformer (ViT) for MRI Images Classification |
| title_sort | transforming alzheimer s disease diagnosis implementing vision transformer vit for mri images classification |
| topic | Alzheimer’s deep learning image classification MRI vision transformer |
| url | https://e-journal.uum.edu.my/index.php/jict/article/view/25840 |
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