Ensemble network using oblique coronal MRI for Alzheimer’s disease diagnosis
Alzheimer’s disease (AD) is a primary degenerative brain disorder commonly found in the elderly, Mild cognitive impairment (MCI) can be considered a transitional stage from normal aging to Alzheimer’s disease. Therefore, distinguishing between normal aging and disease-induced neurofunctional impairm...
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Elsevier
2025-04-01
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| Series: | NeuroImage |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811925001533 |
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| author | Cunhao Li Zhongjian Gao Xiaomei Chen Xuqiang Zheng Xiaoman Zhang Chih-Yang Lin |
| author_facet | Cunhao Li Zhongjian Gao Xiaomei Chen Xuqiang Zheng Xiaoman Zhang Chih-Yang Lin |
| author_sort | Cunhao Li |
| collection | DOAJ |
| description | Alzheimer’s disease (AD) is a primary degenerative brain disorder commonly found in the elderly, Mild cognitive impairment (MCI) can be considered a transitional stage from normal aging to Alzheimer’s disease. Therefore, distinguishing between normal aging and disease-induced neurofunctional impairments is crucial in clinical treatment. Although deep learning methods have been widely applied in Alzheimer’s diagnosis, the varying data formats used by different methods limited their clinical applicability. In this study, based on the ADNI dataset and previous clinical diagnostic experience, we propose a method using oblique coronal MRI to assist in diagnosis. We developed an algorithm to extract oblique coronal slices from 3D MRI data and used these slices to train classification networks. To achieve subject-wise classification based on 2D slices, rather than image-wise classification, we employed ensemble learning methods. This approach fused classification results from different modality images or different positions of the same modality images, constructing a more reliable ensemble classification model. The experiments introduced various decision fusion and feature fusion schemes, demonstrating the potential of oblique coronal MRI slices in assisting diagnosis. Notably, the weighted voting from decision fusion strategy trained on oblique coronal slices achieved accuracy rates of 97.5% for CN vs. AD, 100% for CN vs. MCI, and 94.83% for MCI vs. AD across the three classification tasks. |
| format | Article |
| id | doaj-art-e3dbe1994b3d46f3a7e15e5f05c73b0c |
| institution | OA Journals |
| issn | 1095-9572 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj-art-e3dbe1994b3d46f3a7e15e5f05c73b0c2025-08-20T02:25:45ZengElsevierNeuroImage1095-95722025-04-0131012115110.1016/j.neuroimage.2025.121151Ensemble network using oblique coronal MRI for Alzheimer’s disease diagnosisCunhao Li0Zhongjian Gao1Xiaomei Chen2Xuqiang Zheng3Xiaoman Zhang4Chih-Yang Lin5Key Laboratory of Optoelectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou, ChinaSchool of mechanical and electrical engineering, Sanming University, Sanming, ChinaDepartment of Ophthalmology, Fujian Provincial Hospital North Branch, Fujian Provincial Geriatric Hospital, Fuzhou, ChinaDepartment of Medical Imaging, Fujian Provincial Hospital North Branch, Fujian Provincial Geriatric Hospital, Fuzhou, ChinaKey Laboratory of Optoelectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou, China; Corresponding authors.Department of Mechanical Engineering, National Central University, Taoyuan, Taiwan; Corresponding authors.Alzheimer’s disease (AD) is a primary degenerative brain disorder commonly found in the elderly, Mild cognitive impairment (MCI) can be considered a transitional stage from normal aging to Alzheimer’s disease. Therefore, distinguishing between normal aging and disease-induced neurofunctional impairments is crucial in clinical treatment. Although deep learning methods have been widely applied in Alzheimer’s diagnosis, the varying data formats used by different methods limited their clinical applicability. In this study, based on the ADNI dataset and previous clinical diagnostic experience, we propose a method using oblique coronal MRI to assist in diagnosis. We developed an algorithm to extract oblique coronal slices from 3D MRI data and used these slices to train classification networks. To achieve subject-wise classification based on 2D slices, rather than image-wise classification, we employed ensemble learning methods. This approach fused classification results from different modality images or different positions of the same modality images, constructing a more reliable ensemble classification model. The experiments introduced various decision fusion and feature fusion schemes, demonstrating the potential of oblique coronal MRI slices in assisting diagnosis. Notably, the weighted voting from decision fusion strategy trained on oblique coronal slices achieved accuracy rates of 97.5% for CN vs. AD, 100% for CN vs. MCI, and 94.83% for MCI vs. AD across the three classification tasks.http://www.sciencedirect.com/science/article/pii/S1053811925001533MRIOblique coronal slicesEnsemble networkAlzheimer’s diseaseMild cognitive impairment |
| spellingShingle | Cunhao Li Zhongjian Gao Xiaomei Chen Xuqiang Zheng Xiaoman Zhang Chih-Yang Lin Ensemble network using oblique coronal MRI for Alzheimer’s disease diagnosis NeuroImage MRI Oblique coronal slices Ensemble network Alzheimer’s disease Mild cognitive impairment |
| title | Ensemble network using oblique coronal MRI for Alzheimer’s disease diagnosis |
| title_full | Ensemble network using oblique coronal MRI for Alzheimer’s disease diagnosis |
| title_fullStr | Ensemble network using oblique coronal MRI for Alzheimer’s disease diagnosis |
| title_full_unstemmed | Ensemble network using oblique coronal MRI for Alzheimer’s disease diagnosis |
| title_short | Ensemble network using oblique coronal MRI for Alzheimer’s disease diagnosis |
| title_sort | ensemble network using oblique coronal mri for alzheimer s disease diagnosis |
| topic | MRI Oblique coronal slices Ensemble network Alzheimer’s disease Mild cognitive impairment |
| url | http://www.sciencedirect.com/science/article/pii/S1053811925001533 |
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