Enhanced ROI guided deep learning model for Alzheimer’s detection using 3D MRI images
Alzheimer’s disease is an incurable condition that predominantly affects the human brain, leading to the shrinkage of various brain regions and the disruption of neuronal connections. Current state-of-the-art methods for detecting Alzheimer’s disease using 3D MRI images are resource-intensive and ti...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Informatics in Medicine Unlocked |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352914825000383 |
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| author | Israt Jahan Khan Md. Fahim Bin Amin Md. Delwar Shahadat Deepu Hazera Khatun Hira Asif Mahmud Anas Mashad Chowdhury Salekul Islam Md. Saddam Hossain Mukta Swakkhar Shatabda |
| author_facet | Israt Jahan Khan Md. Fahim Bin Amin Md. Delwar Shahadat Deepu Hazera Khatun Hira Asif Mahmud Anas Mashad Chowdhury Salekul Islam Md. Saddam Hossain Mukta Swakkhar Shatabda |
| author_sort | Israt Jahan Khan |
| collection | DOAJ |
| description | Alzheimer’s disease is an incurable condition that predominantly affects the human brain, leading to the shrinkage of various brain regions and the disruption of neuronal connections. Current state-of-the-art methods for detecting Alzheimer’s disease using 3D MRI images are resource-intensive and time-consuming. In this paper, we propose a Regions of Interest (ROI)-guided detection paradigm to address these challenges. We employ a 3D ResNet integrated with a Convolutional Block Attention Module (CBAM), demonstrating that emphasising ROIs in brain imaging can substantially reduce both computational expenditure and training time. Our model exhibits robust performance in discriminating Alzheimer’s disease from mild cognitive impairment, achieving an accuracy of 88% across the entire brain and 92% within targeted ROIs on the ADNI dataset. The accuracy on the OASIS dataset is even higher, reaching 98% for all regions and 98.33% for the ROIs. When distinguishing Alzheimer’s disease from cognitively normal individuals, the accuracy improves further, achieving 93.33% for the ROIs on the ADNI dataset and 97.8% on the OASIS dataset. In differentiating cognitively normal individuals from those with mild cognitive impairment, the model attains an accuracy of 88.2% for the ROIs on the ADNI dataset and 98.6% on the OASIS dataset. These findings highlight a notable enhancement in detection accuracy through the utilisation of fewer, yet more salient brain regions, underscoring the efficacy of our ROI-guided approach. |
| format | Article |
| id | doaj-art-2f4657e63c184be993c94b626660bc23 |
| institution | DOAJ |
| issn | 2352-9148 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Informatics in Medicine Unlocked |
| spelling | doaj-art-2f4657e63c184be993c94b626660bc232025-08-20T03:10:20ZengElsevierInformatics in Medicine Unlocked2352-91482025-01-015610165010.1016/j.imu.2025.101650Enhanced ROI guided deep learning model for Alzheimer’s detection using 3D MRI imagesIsrat Jahan Khan0Md. Fahim Bin Amin1Md. Delwar Shahadat Deepu2Hazera Khatun Hira3Asif Mahmud4Anas Mashad Chowdhury5Salekul Islam6Md. Saddam Hossain Mukta7Swakkhar Shatabda8Department of Computer Science and Engineering, United International University, BangladeshDepartment of Computer Science and Engineering, United International University, BangladeshDepartment of Computer Science and Engineering, United International University, BangladeshDepartment of Computer Science and Engineering, United International University, BangladeshDepartment of Computer Science and Engineering, United International University, BangladeshDepartment of Computer Science and Engineering, United International University, BangladeshDepartment of Electrical and Computer Engineering, North South University, BangladeshDepartment of Computer Science and Engineering, United International University, Bangladesh; LUT School of Engineering Sciences, Lappeenranta-Lahti University of Technology, FinlandDepartment of Computer Science and Engineering, BRAC University, Bangladesh; Corresponding author.Alzheimer’s disease is an incurable condition that predominantly affects the human brain, leading to the shrinkage of various brain regions and the disruption of neuronal connections. Current state-of-the-art methods for detecting Alzheimer’s disease using 3D MRI images are resource-intensive and time-consuming. In this paper, we propose a Regions of Interest (ROI)-guided detection paradigm to address these challenges. We employ a 3D ResNet integrated with a Convolutional Block Attention Module (CBAM), demonstrating that emphasising ROIs in brain imaging can substantially reduce both computational expenditure and training time. Our model exhibits robust performance in discriminating Alzheimer’s disease from mild cognitive impairment, achieving an accuracy of 88% across the entire brain and 92% within targeted ROIs on the ADNI dataset. The accuracy on the OASIS dataset is even higher, reaching 98% for all regions and 98.33% for the ROIs. When distinguishing Alzheimer’s disease from cognitively normal individuals, the accuracy improves further, achieving 93.33% for the ROIs on the ADNI dataset and 97.8% on the OASIS dataset. In differentiating cognitively normal individuals from those with mild cognitive impairment, the model attains an accuracy of 88.2% for the ROIs on the ADNI dataset and 98.6% on the OASIS dataset. These findings highlight a notable enhancement in detection accuracy through the utilisation of fewer, yet more salient brain regions, underscoring the efficacy of our ROI-guided approach.http://www.sciencedirect.com/science/article/pii/S2352914825000383Alzheimer’s diseaseRegions of Interest (ROIs)3D MRITransfer learningResNet 3DEfficient computation |
| spellingShingle | Israt Jahan Khan Md. Fahim Bin Amin Md. Delwar Shahadat Deepu Hazera Khatun Hira Asif Mahmud Anas Mashad Chowdhury Salekul Islam Md. Saddam Hossain Mukta Swakkhar Shatabda Enhanced ROI guided deep learning model for Alzheimer’s detection using 3D MRI images Informatics in Medicine Unlocked Alzheimer’s disease Regions of Interest (ROIs) 3D MRI Transfer learning ResNet 3D Efficient computation |
| title | Enhanced ROI guided deep learning model for Alzheimer’s detection using 3D MRI images |
| title_full | Enhanced ROI guided deep learning model for Alzheimer’s detection using 3D MRI images |
| title_fullStr | Enhanced ROI guided deep learning model for Alzheimer’s detection using 3D MRI images |
| title_full_unstemmed | Enhanced ROI guided deep learning model for Alzheimer’s detection using 3D MRI images |
| title_short | Enhanced ROI guided deep learning model for Alzheimer’s detection using 3D MRI images |
| title_sort | enhanced roi guided deep learning model for alzheimer s detection using 3d mri images |
| topic | Alzheimer’s disease Regions of Interest (ROIs) 3D MRI Transfer learning ResNet 3D Efficient computation |
| url | http://www.sciencedirect.com/science/article/pii/S2352914825000383 |
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