Classification of Alzheimer’s Progression Using fMRI Data
In the last three decades, the development of functional magnetic resonance imaging (fMRI) has significantly contributed to the understanding of the brain, functional brain mapping, and resting-state brain networks. Given the recent successes of deep learning in various fields, we propose a 3D-CNN-L...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/14/6330 |
| _version_ | 1797587505688608768 |
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| author | Ju-Hyeon Noh Jun-Hyeok Kim Hee-Deok Yang |
| author_facet | Ju-Hyeon Noh Jun-Hyeok Kim Hee-Deok Yang |
| author_sort | Ju-Hyeon Noh |
| collection | DOAJ |
| description | In the last three decades, the development of functional magnetic resonance imaging (fMRI) has significantly contributed to the understanding of the brain, functional brain mapping, and resting-state brain networks. Given the recent successes of deep learning in various fields, we propose a 3D-CNN-LSTM classification model to diagnose health conditions with the following classes: condition normal (CN), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and Alzheimer’s disease (AD). The proposed method employs spatial and temporal feature extractors, wherein the former utilizes a U-Net architecture to extract spatial features, and the latter utilizes long short-term memory (LSTM) to extract temporal features. Prior to feature extraction, we performed four-step pre-processing to remove noise from the fMRI data. In the comparative experiments, we trained each of the three models by adjusting the time dimension. The network exhibited an average accuracy of 96.4% when using five-fold cross-validation. These results show that the proposed method has high potential for identifying the progression of Alzheimer’s by analyzing 4D fMRI data. |
| first_indexed | 2024-03-11T00:40:52Z |
| format | Article |
| id | doaj.art-3fc4337328494008a0bfa4425e4d0255 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T00:40:52Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-3fc4337328494008a0bfa4425e4d02552023-11-18T21:16:13ZengMDPI AGSensors1424-82202023-07-012314633010.3390/s23146330Classification of Alzheimer’s Progression Using fMRI DataJu-Hyeon Noh0Jun-Hyeok Kim1Hee-Deok Yang2Department of Computer Engineering, University of Chosun, Gwangju 61452, Republic of KoreaDepartment of Computer Engineering, University of Chosun, Gwangju 61452, Republic of KoreaDepartment of Computer Engineering, University of Chosun, Gwangju 61452, Republic of KoreaIn the last three decades, the development of functional magnetic resonance imaging (fMRI) has significantly contributed to the understanding of the brain, functional brain mapping, and resting-state brain networks. Given the recent successes of deep learning in various fields, we propose a 3D-CNN-LSTM classification model to diagnose health conditions with the following classes: condition normal (CN), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and Alzheimer’s disease (AD). The proposed method employs spatial and temporal feature extractors, wherein the former utilizes a U-Net architecture to extract spatial features, and the latter utilizes long short-term memory (LSTM) to extract temporal features. Prior to feature extraction, we performed four-step pre-processing to remove noise from the fMRI data. In the comparative experiments, we trained each of the three models by adjusting the time dimension. The network exhibited an average accuracy of 96.4% when using five-fold cross-validation. These results show that the proposed method has high potential for identifying the progression of Alzheimer’s by analyzing 4D fMRI data.https://www.mdpi.com/1424-8220/23/14/6330Alzheimer’s disease3D U-Netdeep learning |
| spellingShingle | Ju-Hyeon Noh Jun-Hyeok Kim Hee-Deok Yang Classification of Alzheimer’s Progression Using fMRI Data Sensors Alzheimer’s disease 3D U-Net deep learning |
| title | Classification of Alzheimer’s Progression Using fMRI Data |
| title_full | Classification of Alzheimer’s Progression Using fMRI Data |
| title_fullStr | Classification of Alzheimer’s Progression Using fMRI Data |
| title_full_unstemmed | Classification of Alzheimer’s Progression Using fMRI Data |
| title_short | Classification of Alzheimer’s Progression Using fMRI Data |
| title_sort | classification of alzheimer s progression using fmri data |
| topic | Alzheimer’s disease 3D U-Net deep learning |
| url | https://www.mdpi.com/1424-8220/23/14/6330 |
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