Vision transformer-equipped Convolutional Neural Networks for automated Alzheimer's disease diagnosis using 3D MRI scans
Alzheimer's disease (AD) is a neurodegenerative ailment that is becoming increasingly common, making it a major worldwide health concern. Effective care depends on an early and correct diagnosis, but traditional diagnostic techniques are frequently constrained by subjectivity and expensive cost...
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| Format: | Article |
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Frontiers Media S.A.
2024-12-01
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| Series: | Frontiers in Neurology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fneur.2024.1490829/full |
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| author | Zhen Zhao Pauline Shan Qing Yeoh Xiaowei Zuo Joon Huang Chuah Chee-Onn Chow Xiang Wu Khin Wee Lai |
| author_facet | Zhen Zhao Pauline Shan Qing Yeoh Xiaowei Zuo Joon Huang Chuah Chee-Onn Chow Xiang Wu Khin Wee Lai |
| author_sort | Zhen Zhao |
| collection | DOAJ |
| description | Alzheimer's disease (AD) is a neurodegenerative ailment that is becoming increasingly common, making it a major worldwide health concern. Effective care depends on an early and correct diagnosis, but traditional diagnostic techniques are frequently constrained by subjectivity and expensive costs. This study proposes a novel Vision Transformer-equipped Convolutional Neural Networks (VECNN) that uses three-dimensional magnetic resonance imaging to improve diagnosis accuracy. Utilizing the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, which comprised 2,248 3D MRI images and diverse patient demographics, the proposed model achieved an accuracy of 92.14%, a precision of 86.84%, a sensitivity of 93.27%, and a specificity of 89.95% in distinguishing between AD, healthy controls (HC), and moderate cognitive impairment (MCI). The findings suggest that VECNN can be a valuable tool in clinical settings, providing a non-invasive, cost-effective, and objective diagnostic technique. This research opens the door for future advancements in early diagnosis and personalized therapy for Alzheimer's Disease. |
| format | Article |
| id | doaj-art-d1b3d8757a57456794b1a9d07d89aadd |
| institution | Kabale University |
| issn | 1664-2295 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurology |
| spelling | doaj-art-d1b3d8757a57456794b1a9d07d89aadd2024-12-16T05:10:18ZengFrontiers Media S.A.Frontiers in Neurology1664-22952024-12-011510.3389/fneur.2024.14908291490829Vision transformer-equipped Convolutional Neural Networks for automated Alzheimer's disease diagnosis using 3D MRI scansZhen Zhao0Pauline Shan Qing Yeoh1Xiaowei Zuo2Joon Huang Chuah3Chee-Onn Chow4Xiang Wu5Khin Wee Lai6Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, MalaysiaDepartment of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, MalaysiaDepartment of Psychiatry, The Affiliated Xuzhou Oriental Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, ChinaDepartment of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, MalaysiaDepartment of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, MalaysiaSchool of Medical Information Engineering, Xuzhou Medical University, Xuzhou, Jiangsu, ChinaDepartment of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, MalaysiaAlzheimer's disease (AD) is a neurodegenerative ailment that is becoming increasingly common, making it a major worldwide health concern. Effective care depends on an early and correct diagnosis, but traditional diagnostic techniques are frequently constrained by subjectivity and expensive costs. This study proposes a novel Vision Transformer-equipped Convolutional Neural Networks (VECNN) that uses three-dimensional magnetic resonance imaging to improve diagnosis accuracy. Utilizing the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, which comprised 2,248 3D MRI images and diverse patient demographics, the proposed model achieved an accuracy of 92.14%, a precision of 86.84%, a sensitivity of 93.27%, and a specificity of 89.95% in distinguishing between AD, healthy controls (HC), and moderate cognitive impairment (MCI). The findings suggest that VECNN can be a valuable tool in clinical settings, providing a non-invasive, cost-effective, and objective diagnostic technique. This research opens the door for future advancements in early diagnosis and personalized therapy for Alzheimer's Disease.https://www.frontiersin.org/articles/10.3389/fneur.2024.1490829/fullAlzheimer's diseaseclassificationConvolutional Neural Networkmagnetic resonance imagingtransformer |
| spellingShingle | Zhen Zhao Pauline Shan Qing Yeoh Xiaowei Zuo Joon Huang Chuah Chee-Onn Chow Xiang Wu Khin Wee Lai Vision transformer-equipped Convolutional Neural Networks for automated Alzheimer's disease diagnosis using 3D MRI scans Frontiers in Neurology Alzheimer's disease classification Convolutional Neural Network magnetic resonance imaging transformer |
| title | Vision transformer-equipped Convolutional Neural Networks for automated Alzheimer's disease diagnosis using 3D MRI scans |
| title_full | Vision transformer-equipped Convolutional Neural Networks for automated Alzheimer's disease diagnosis using 3D MRI scans |
| title_fullStr | Vision transformer-equipped Convolutional Neural Networks for automated Alzheimer's disease diagnosis using 3D MRI scans |
| title_full_unstemmed | Vision transformer-equipped Convolutional Neural Networks for automated Alzheimer's disease diagnosis using 3D MRI scans |
| title_short | Vision transformer-equipped Convolutional Neural Networks for automated Alzheimer's disease diagnosis using 3D MRI scans |
| title_sort | vision transformer equipped convolutional neural networks for automated alzheimer s disease diagnosis using 3d mri scans |
| topic | Alzheimer's disease classification Convolutional Neural Network magnetic resonance imaging transformer |
| url | https://www.frontiersin.org/articles/10.3389/fneur.2024.1490829/full |
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