Alzheimer’s disease diagnosis by 3D-SEConvNeXt
Abstract Alzheimer’s disease (AD) constitutes a fatal neurodegenerative disorder and represents the most prevalent form of dementia among the elderly population. Traditional manual AD classification methods, such as clinical diagnosis, are known to be time-consuming and labor-intensive, with relativ...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-01-01
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| Series: | Journal of Big Data |
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| Online Access: | https://doi.org/10.1186/s40537-025-01088-8 |
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| author | Zhongyi Hu Yuhang Wang Lei Xiao |
| author_facet | Zhongyi Hu Yuhang Wang Lei Xiao |
| author_sort | Zhongyi Hu |
| collection | DOAJ |
| description | Abstract Alzheimer’s disease (AD) constitutes a fatal neurodegenerative disorder and represents the most prevalent form of dementia among the elderly population. Traditional manual AD classification methods, such as clinical diagnosis, are known to be time-consuming and labor-intensive, with relatively low accuracy. Therefore, our work aims to develop a new deep learning framework to tackle this challenge. Our proposed model integrates ConvNeXt with three-dimensional (3D) convolution and incorporates a 3D Squeeze-and-Excitation (3D-SE) attention mechanism to enhance early classification of AD. The experimental data is sourced from the publicly accessible Alzheimer’s disease Neuroimaging Initiative (ADNI) database, with raw Magnetic Resonance Imaging (MRI) data preprocessed using SPM12 software. Subsequently, the preprocessed data is input into the 3D-SEConvNeXt network to perform four classification tasks: distinguishing between AD and Normal Control (NC), Mild Cognitive Impairment (MCI) and NC, AD and MCI, as well as AD, MCI, and NC. The experimental results indicate that the 3D-SEConvNeXt model consistently outperforms alternative models in terms of accuracy, achieving commendable outcomes in early AD diagnostic tasks. |
| format | Article |
| id | doaj-art-159de4c9c8524a31a7c7d9fbcd6063c5 |
| institution | Kabale University |
| issn | 2196-1115 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Big Data |
| spelling | doaj-art-159de4c9c8524a31a7c7d9fbcd6063c52025-02-02T12:28:32ZengSpringerOpenJournal of Big Data2196-11152025-01-0112111310.1186/s40537-025-01088-8Alzheimer’s disease diagnosis by 3D-SEConvNeXtZhongyi Hu0Yuhang Wang1Lei Xiao2College of Computer Science and Artificial Intelligence, Wenzhou UniversityCollege of Computer Science and Artificial Intelligence, Wenzhou UniversityCollege of Computer Science and Artificial Intelligence, Wenzhou UniversityAbstract Alzheimer’s disease (AD) constitutes a fatal neurodegenerative disorder and represents the most prevalent form of dementia among the elderly population. Traditional manual AD classification methods, such as clinical diagnosis, are known to be time-consuming and labor-intensive, with relatively low accuracy. Therefore, our work aims to develop a new deep learning framework to tackle this challenge. Our proposed model integrates ConvNeXt with three-dimensional (3D) convolution and incorporates a 3D Squeeze-and-Excitation (3D-SE) attention mechanism to enhance early classification of AD. The experimental data is sourced from the publicly accessible Alzheimer’s disease Neuroimaging Initiative (ADNI) database, with raw Magnetic Resonance Imaging (MRI) data preprocessed using SPM12 software. Subsequently, the preprocessed data is input into the 3D-SEConvNeXt network to perform four classification tasks: distinguishing between AD and Normal Control (NC), Mild Cognitive Impairment (MCI) and NC, AD and MCI, as well as AD, MCI, and NC. The experimental results indicate that the 3D-SEConvNeXt model consistently outperforms alternative models in terms of accuracy, achieving commendable outcomes in early AD diagnostic tasks.https://doi.org/10.1186/s40537-025-01088-8Alzheimer’s diseaseComputer-aided diagnosis3D convolutional neural networkImage classification |
| spellingShingle | Zhongyi Hu Yuhang Wang Lei Xiao Alzheimer’s disease diagnosis by 3D-SEConvNeXt Journal of Big Data Alzheimer’s disease Computer-aided diagnosis 3D convolutional neural network Image classification |
| title | Alzheimer’s disease diagnosis by 3D-SEConvNeXt |
| title_full | Alzheimer’s disease diagnosis by 3D-SEConvNeXt |
| title_fullStr | Alzheimer’s disease diagnosis by 3D-SEConvNeXt |
| title_full_unstemmed | Alzheimer’s disease diagnosis by 3D-SEConvNeXt |
| title_short | Alzheimer’s disease diagnosis by 3D-SEConvNeXt |
| title_sort | alzheimer s disease diagnosis by 3d seconvnext |
| topic | Alzheimer’s disease Computer-aided diagnosis 3D convolutional neural network Image classification |
| url | https://doi.org/10.1186/s40537-025-01088-8 |
| work_keys_str_mv | AT zhongyihu alzheimersdiseasediagnosisby3dseconvnext AT yuhangwang alzheimersdiseasediagnosisby3dseconvnext AT leixiao alzheimersdiseasediagnosisby3dseconvnext |