MRI-Driven Alzheimer’s Disease Diagnosis Using Deep Network Fusion and Optimal Selection of Feature
Alzheimer’s disease (AD) is a degenerative neurological condition characterized by cognitive decline, memory loss, and reduced everyday function, which eventually causes dementia. Symptoms develop years after the disease begins, making early detection difficult. While AD remains incurable, timely de...
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MDPI AG
2024-10-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/11/11/1076 |
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| author | Muhammad Umair Ali Shaik Javeed Hussain Majdi Khalid Majed Farrash Hassan Fareed M. Lahza Amad Zafar |
| author_facet | Muhammad Umair Ali Shaik Javeed Hussain Majdi Khalid Majed Farrash Hassan Fareed M. Lahza Amad Zafar |
| author_sort | Muhammad Umair Ali |
| collection | DOAJ |
| description | Alzheimer’s disease (AD) is a degenerative neurological condition characterized by cognitive decline, memory loss, and reduced everyday function, which eventually causes dementia. Symptoms develop years after the disease begins, making early detection difficult. While AD remains incurable, timely detection and prompt treatment can substantially slow its progression. This study presented a framework for automated AD detection using brain MRIs. Firstly, the deep network information (i.e., features) were extracted using various deep-learning networks. The information extracted from the best deep networks (EfficientNet-b0 and MobileNet-v2) were merged using the canonical correlation approach (CCA). The CCA-based fused features resulted in an enhanced classification performance of 94.7% with a large feature vector size (i.e., 2532). To remove the redundant features from the CCA-based fused feature vector, the binary-enhanced WOA was utilized for optimal feature selection, which yielded an average accuracy of 98.12 ± 0.52 (mean ± standard deviation) with only 953 features. The results were compared with other optimal feature selection techniques, showing that the binary-enhanced WOA results are statistically significant (<i>p</i> < 0.01). The ablation study was also performed to show the significance of each step of the proposed methodology. Furthermore, the comparison shows the superiority and high classification performance of the proposed automated AD detection approach, suggesting that the hybrid approach may help doctors with dementia detection and staging. |
| format | Article |
| id | doaj-art-7ff596f528cf4999b1a37e40b8d5355c |
| institution | OA Journals |
| issn | 2306-5354 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
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| series | Bioengineering |
| spelling | doaj-art-7ff596f528cf4999b1a37e40b8d5355c2025-08-20T02:28:01ZengMDPI AGBioengineering2306-53542024-10-011111107610.3390/bioengineering11111076MRI-Driven Alzheimer’s Disease Diagnosis Using Deep Network Fusion and Optimal Selection of FeatureMuhammad Umair Ali0Shaik Javeed Hussain1Majdi Khalid2Majed Farrash3Hassan Fareed M. Lahza4Amad Zafar5Department of Artificial Intelligence and Robotics, Sejong University, Seoul 05006, Republic of KoreaDepartment of Electrical and Electronics, Global College of Engineering and Technology, Muscat 112, OmanDepartment of Computer Science and Artificial Intelligence, College of Computing, Umm Al-Qura University, Makkah 24382, Saudi ArabiaDepartment of Computer Science and Artificial Intelligence, College of Computing, Umm Al-Qura University, Makkah 24382, Saudi ArabiaDepartment of Cybersecurity, College of Computing Umm Al-Qura University, Makkah 24382, Saudi ArabiaDepartment of Artificial Intelligence and Robotics, Sejong University, Seoul 05006, Republic of KoreaAlzheimer’s disease (AD) is a degenerative neurological condition characterized by cognitive decline, memory loss, and reduced everyday function, which eventually causes dementia. Symptoms develop years after the disease begins, making early detection difficult. While AD remains incurable, timely detection and prompt treatment can substantially slow its progression. This study presented a framework for automated AD detection using brain MRIs. Firstly, the deep network information (i.e., features) were extracted using various deep-learning networks. The information extracted from the best deep networks (EfficientNet-b0 and MobileNet-v2) were merged using the canonical correlation approach (CCA). The CCA-based fused features resulted in an enhanced classification performance of 94.7% with a large feature vector size (i.e., 2532). To remove the redundant features from the CCA-based fused feature vector, the binary-enhanced WOA was utilized for optimal feature selection, which yielded an average accuracy of 98.12 ± 0.52 (mean ± standard deviation) with only 953 features. The results were compared with other optimal feature selection techniques, showing that the binary-enhanced WOA results are statistically significant (<i>p</i> < 0.01). The ablation study was also performed to show the significance of each step of the proposed methodology. Furthermore, the comparison shows the superiority and high classification performance of the proposed automated AD detection approach, suggesting that the hybrid approach may help doctors with dementia detection and staging.https://www.mdpi.com/2306-5354/11/11/1076Alzheimer diseasedementiadeep featuresfeature fusionfeature selectioncanonical correlation analysis |
| spellingShingle | Muhammad Umair Ali Shaik Javeed Hussain Majdi Khalid Majed Farrash Hassan Fareed M. Lahza Amad Zafar MRI-Driven Alzheimer’s Disease Diagnosis Using Deep Network Fusion and Optimal Selection of Feature Bioengineering Alzheimer disease dementia deep features feature fusion feature selection canonical correlation analysis |
| title | MRI-Driven Alzheimer’s Disease Diagnosis Using Deep Network Fusion and Optimal Selection of Feature |
| title_full | MRI-Driven Alzheimer’s Disease Diagnosis Using Deep Network Fusion and Optimal Selection of Feature |
| title_fullStr | MRI-Driven Alzheimer’s Disease Diagnosis Using Deep Network Fusion and Optimal Selection of Feature |
| title_full_unstemmed | MRI-Driven Alzheimer’s Disease Diagnosis Using Deep Network Fusion and Optimal Selection of Feature |
| title_short | MRI-Driven Alzheimer’s Disease Diagnosis Using Deep Network Fusion and Optimal Selection of Feature |
| title_sort | mri driven alzheimer s disease diagnosis using deep network fusion and optimal selection of feature |
| topic | Alzheimer disease dementia deep features feature fusion feature selection canonical correlation analysis |
| url | https://www.mdpi.com/2306-5354/11/11/1076 |
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