Advancing Early Diagnosis: Predicting Mild Cognitive Impairment Progression in Normal Individuals Using Deep Learning on MRI Features
One of the most challenging tasks for neurologists is the early diagnosis of Alzheimer’s disease (AD). Early and accurate diagnosis of the mild cognitive impairment (MCI) stage can enhance efforts to slow down the major consequences linked to this condition. Deep learning systems provide...
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| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11079556/ |
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| author | Abdullah Baktash Yashar Sarbaz Saeed Meshgini Reza Afrouzian |
| author_facet | Abdullah Baktash Yashar Sarbaz Saeed Meshgini Reza Afrouzian |
| author_sort | Abdullah Baktash |
| collection | DOAJ |
| description | One of the most challenging tasks for neurologists is the early diagnosis of Alzheimer’s disease (AD). Early and accurate diagnosis of the mild cognitive impairment (MCI) stage can enhance efforts to slow down the major consequences linked to this condition. Deep learning systems provide a promising performance in diagnosing the disease through neuroimaging analysis. This research aims to develop a deep learning-based system that efficiently identifies and analyzes the progression from Cognitively Normal (CN) to MCI, addressing the growing need for more accessible, accurate diagnostic tools. The proposed model comprises two distinct feature extraction paths to capture local and global image features. Each path includes advanced modules for feature refinement associated with the channel attention mechanism. The resultant output features are produced using a learned fusion technique from the two paths’ features and applied to the CN vs. MCI binary classifier. Furthermore, the proposed Suspected Subject Classifier (SSC) system applies various machine-learning methods to identify the suspected MCI subjects. The results showed a comparative performance for the binary diagnosis of CN individuals and those with MCI, achieving an accuracy of 91.6% and 88.4% for multi-class diagnoses, including the prediction of progression from normal to confirmed MCI. This study represents an exceptional stride toward predicting early MCI in normal individuals. By enhancing prediction efficiency for early disease progression in normal individuals, our method can potentially advance intervention strategies and improve patient care outcomes. |
| format | Article |
| id | doaj-art-9fd851a3f0c84a8eb35eeb96fcef20a7 |
| institution | Matheson Library |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-9fd851a3f0c84a8eb35eeb96fcef20a72025-07-24T23:01:13ZengIEEEIEEE Access2169-35362025-01-011312259112260210.1109/ACCESS.2025.358880111079556Advancing Early Diagnosis: Predicting Mild Cognitive Impairment Progression in Normal Individuals Using Deep Learning on MRI FeaturesAbdullah Baktash0https://orcid.org/0000-0002-4464-3767Yashar Sarbaz1https://orcid.org/0000-0002-6258-9653Saeed Meshgini2Reza Afrouzian3https://orcid.org/0000-0002-6968-0409Biomedical Engineering Department, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, IranBiomedical Engineering Department, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, IranBiomedical Engineering Department, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, IranMiyaneh Faculty of Engineering, University of Tabriz, Miyaneh, IranOne of the most challenging tasks for neurologists is the early diagnosis of Alzheimer’s disease (AD). Early and accurate diagnosis of the mild cognitive impairment (MCI) stage can enhance efforts to slow down the major consequences linked to this condition. Deep learning systems provide a promising performance in diagnosing the disease through neuroimaging analysis. This research aims to develop a deep learning-based system that efficiently identifies and analyzes the progression from Cognitively Normal (CN) to MCI, addressing the growing need for more accessible, accurate diagnostic tools. The proposed model comprises two distinct feature extraction paths to capture local and global image features. Each path includes advanced modules for feature refinement associated with the channel attention mechanism. The resultant output features are produced using a learned fusion technique from the two paths’ features and applied to the CN vs. MCI binary classifier. Furthermore, the proposed Suspected Subject Classifier (SSC) system applies various machine-learning methods to identify the suspected MCI subjects. The results showed a comparative performance for the binary diagnosis of CN individuals and those with MCI, achieving an accuracy of 91.6% and 88.4% for multi-class diagnoses, including the prediction of progression from normal to confirmed MCI. This study represents an exceptional stride toward predicting early MCI in normal individuals. By enhancing prediction efficiency for early disease progression in normal individuals, our method can potentially advance intervention strategies and improve patient care outcomes.https://ieeexplore.ieee.org/document/11079556/Alzheimer’s diseasedual path convolutional neural networksearly mild cognitive impairmentchannel attentionspatial attention |
| spellingShingle | Abdullah Baktash Yashar Sarbaz Saeed Meshgini Reza Afrouzian Advancing Early Diagnosis: Predicting Mild Cognitive Impairment Progression in Normal Individuals Using Deep Learning on MRI Features IEEE Access Alzheimer’s disease dual path convolutional neural networks early mild cognitive impairment channel attention spatial attention |
| title | Advancing Early Diagnosis: Predicting Mild Cognitive Impairment Progression in Normal Individuals Using Deep Learning on MRI Features |
| title_full | Advancing Early Diagnosis: Predicting Mild Cognitive Impairment Progression in Normal Individuals Using Deep Learning on MRI Features |
| title_fullStr | Advancing Early Diagnosis: Predicting Mild Cognitive Impairment Progression in Normal Individuals Using Deep Learning on MRI Features |
| title_full_unstemmed | Advancing Early Diagnosis: Predicting Mild Cognitive Impairment Progression in Normal Individuals Using Deep Learning on MRI Features |
| title_short | Advancing Early Diagnosis: Predicting Mild Cognitive Impairment Progression in Normal Individuals Using Deep Learning on MRI Features |
| title_sort | advancing early diagnosis predicting mild cognitive impairment progression in normal individuals using deep learning on mri features |
| topic | Alzheimer’s disease dual path convolutional neural networks early mild cognitive impairment channel attention spatial attention |
| url | https://ieeexplore.ieee.org/document/11079556/ |
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