Attention-driven hybrid deep learning and SVM model for early Alzheimer’s diagnosis using neuroimaging fusion
Abstract Alzheimer’s Disease (AD) poses a significant global health challenge, necessitating early and accurate diagnosis to enable timely interventions. AD is a progressive neurodegenerative disorder that affects millions worldwide and is one of the leading causes of cognitive impairment in older a...
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BMC
2025-07-01
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| Series: | BMC Medical Informatics and Decision Making |
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| Online Access: | https://doi.org/10.1186/s12911-025-03073-w |
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| author | Arjun Kidavunil Paduvilan Godlin Atlas Lawrence Livingston Sampath Kumar Kuppuchamy Rajesh Kumar Dhanaraj Muthuvel Subramanian Amal Al-Rasheed Masresha Getahun Ben Othman Soufiene |
| author_facet | Arjun Kidavunil Paduvilan Godlin Atlas Lawrence Livingston Sampath Kumar Kuppuchamy Rajesh Kumar Dhanaraj Muthuvel Subramanian Amal Al-Rasheed Masresha Getahun Ben Othman Soufiene |
| author_sort | Arjun Kidavunil Paduvilan |
| collection | DOAJ |
| description | Abstract Alzheimer’s Disease (AD) poses a significant global health challenge, necessitating early and accurate diagnosis to enable timely interventions. AD is a progressive neurodegenerative disorder that affects millions worldwide and is one of the leading causes of cognitive impairment in older adults. Early diagnosis is critical for enabling effective treatment strategies, slowing disease progression, and improving the quality of life for patients. Existing diagnostic methods often struggle with limited sensitivity, overfitting, and reduced reliability due to inadequate feature extraction, imbalanced datasets, and suboptimal model architectures. This study addresses these gaps by introducing an innovative methodology that combines SVM with Deep Learning (DL) to improve the classification performance of AD. Deep learning models extract high-level imaging features which are then concatenated with SVM kernels in a late-fusion ensemble. This hybrid design leverages deep representations for pattern recognition and SVM’s robustness on small sample sets. This study provides a necessary tool for early-stage identification of possible cases, so enhancing the management and treatment options. This is attained by precisely classifying the disease from neuroimaging data. The approach integrates advanced data pre-processing, dynamic feature optimization, and attention-driven learning mechanisms to enhance interpretability and robustness. The research leverages a dataset of MRI and PET imaging, integrating novel fusion techniques to extract key biomarkers indicative of cognitive decline. Unlike prior approaches, this method effectively mitigates the challenges of data sparsity and dimensionality reduction while improving generalization across diverse datasets. Comparative analysis highlights a 15% improvement in accuracy, a 12% reduction in false positives, and a 10% increase in F1-score against state-of-the-art models such as HNC and MFNNC. The proposed method significantly outperforms existing techniques across metrics like accuracy, sensitivity, specificity, and computational efficiency, achieving an overall accuracy of 98.5%. |
| format | Article |
| id | doaj-art-aa860beef4104341a005d7bf3b0d99d8 |
| institution | DOAJ |
| issn | 1472-6947 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj-art-aa860beef4104341a005d7bf3b0d99d82025-08-20T03:03:41ZengBMCBMC Medical Informatics and Decision Making1472-69472025-07-0125111910.1186/s12911-025-03073-wAttention-driven hybrid deep learning and SVM model for early Alzheimer’s diagnosis using neuroimaging fusionArjun Kidavunil Paduvilan0Godlin Atlas Lawrence Livingston1Sampath Kumar Kuppuchamy2Rajesh Kumar Dhanaraj3Muthuvel Subramanian4Amal Al-Rasheed5Masresha Getahun6Ben Othman Soufiene7Department of Computer Science and Engineering, GITAM UniversityDepartment of Computer Science and Engineering, Bharath Institute of Higher Education and ResearchDepartment of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha UniversitySymbiosis Institute of Computer Studies and Research (SICSR), Symbiosis International (Deemed University)Department of Computer Science and Engineering, AMET UniversityDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman UniversityDepartment of Computer Science and Information Technology, College of Engineering and Technology, Kebri Dehar UniversityPRINCE Laboratory Research, ISITcom, Hammam Sousse, University of SousseAbstract Alzheimer’s Disease (AD) poses a significant global health challenge, necessitating early and accurate diagnosis to enable timely interventions. AD is a progressive neurodegenerative disorder that affects millions worldwide and is one of the leading causes of cognitive impairment in older adults. Early diagnosis is critical for enabling effective treatment strategies, slowing disease progression, and improving the quality of life for patients. Existing diagnostic methods often struggle with limited sensitivity, overfitting, and reduced reliability due to inadequate feature extraction, imbalanced datasets, and suboptimal model architectures. This study addresses these gaps by introducing an innovative methodology that combines SVM with Deep Learning (DL) to improve the classification performance of AD. Deep learning models extract high-level imaging features which are then concatenated with SVM kernels in a late-fusion ensemble. This hybrid design leverages deep representations for pattern recognition and SVM’s robustness on small sample sets. This study provides a necessary tool for early-stage identification of possible cases, so enhancing the management and treatment options. This is attained by precisely classifying the disease from neuroimaging data. The approach integrates advanced data pre-processing, dynamic feature optimization, and attention-driven learning mechanisms to enhance interpretability and robustness. The research leverages a dataset of MRI and PET imaging, integrating novel fusion techniques to extract key biomarkers indicative of cognitive decline. Unlike prior approaches, this method effectively mitigates the challenges of data sparsity and dimensionality reduction while improving generalization across diverse datasets. Comparative analysis highlights a 15% improvement in accuracy, a 12% reduction in false positives, and a 10% increase in F1-score against state-of-the-art models such as HNC and MFNNC. The proposed method significantly outperforms existing techniques across metrics like accuracy, sensitivity, specificity, and computational efficiency, achieving an overall accuracy of 98.5%.https://doi.org/10.1186/s12911-025-03073-wNeuroimagingMachine learningAlzheimer's disease (AD)Disease detectionClassification accuracyRecursive feature elimination |
| spellingShingle | Arjun Kidavunil Paduvilan Godlin Atlas Lawrence Livingston Sampath Kumar Kuppuchamy Rajesh Kumar Dhanaraj Muthuvel Subramanian Amal Al-Rasheed Masresha Getahun Ben Othman Soufiene Attention-driven hybrid deep learning and SVM model for early Alzheimer’s diagnosis using neuroimaging fusion BMC Medical Informatics and Decision Making Neuroimaging Machine learning Alzheimer's disease (AD) Disease detection Classification accuracy Recursive feature elimination |
| title | Attention-driven hybrid deep learning and SVM model for early Alzheimer’s diagnosis using neuroimaging fusion |
| title_full | Attention-driven hybrid deep learning and SVM model for early Alzheimer’s diagnosis using neuroimaging fusion |
| title_fullStr | Attention-driven hybrid deep learning and SVM model for early Alzheimer’s diagnosis using neuroimaging fusion |
| title_full_unstemmed | Attention-driven hybrid deep learning and SVM model for early Alzheimer’s diagnosis using neuroimaging fusion |
| title_short | Attention-driven hybrid deep learning and SVM model for early Alzheimer’s diagnosis using neuroimaging fusion |
| title_sort | attention driven hybrid deep learning and svm model for early alzheimer s diagnosis using neuroimaging fusion |
| topic | Neuroimaging Machine learning Alzheimer's disease (AD) Disease detection Classification accuracy Recursive feature elimination |
| url | https://doi.org/10.1186/s12911-025-03073-w |
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