AlzFusionNet: An Explainable Multimodal Deep Learning Framework Fusing EfficientNet-B7 MRI Embeddings with Clinical Features for Four-Stage Alzheimer’s Disease Classification
DOI:
https://doi.org/10.29070/dzgxgj14Keywords:
Alzheimer’s disease, multimodal deep learning, feature fusion, EfficientNet-B7, structural MRI, dementia staging, explainable artificial intelligence, Grad-CAM, SHAP, clinical decision supportAbstract
Most published artificial-intelligence systems for Alzheimer’s disease operate on a single modality and formulate the task as a binary demented/non-demented decision, which neither exploits the complementary evidence in imaging and cognitive assessment nor resolves the intermediate stages that determine treatment choice. This paper presents AlzFusionNet, a multimodal deep learning framework that jointly models structural magnetic resonance imaging and structured clinical variables for four-stage Alzheimer’s classification. An EfficientNet-B7 backbone, initialised with ImageNet weights and fine-tuned on Alzheimer’s MRI, encodes anatomical patterns including cortical thinning and hippocampal atrophy. In parallel, clinical attributes — age, education, socioeconomic status, Mini-Mental State Examination and Clinical Dementia Rating — are imputed, standardised, reduced by principal component analysis and embedded through a dense subnetwork. The two embeddings are concatenated in a fusion layer and passed to a softmax classification head over Non-Demented, Very Mild, Mild and Moderate Demented classes. Imaging experiments used 664 preprocessed scans (200 Mild, 64 Moderate, 200 Non-Demented, 200 Very Mild) split 70:20:10. Among unimodal deep baselines the plain convolutional network was strongest at 84% validation accuracy, while VGG16, ResNet, AlexNet and EfficientNet-B7 reached 54.27%, 45.23%, 55.78% and 53.02% respectively, indicating that transfer from natural images is inefficient at this dataset scale. Fusion changed the picture substantially: AlzFusionNet attained 96.8% accuracy, 95.7% precision, 97.3% recall, 96.5% F1-score and 98.2% ROC-AUC, against 85.2% for clinical features alone and 91.5% for MRI alone, at a cost of 68.9 million parameters and approximately eight hours of training. Gradient-weighted class activation mapping localised model attention to the hippocampus, entorhinal cortex and temporal lobe, and SHapley Additive exPlanations ranked MMSE, CDR and normalised whole-brain volume as the dominant clinical contributors, indicating that predictions rest on biologically plausible evidence rather than incidental image structure.
Downloads
References
1. World Health Organization, “Dementia,” 2022. [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/dementia
2. C. H. Chang, C. H. Lin, and H. Y. Lane, “Machine learning and novel biomarkers for the diagnosis of Alzheimer’s disease,” International Journal of Molecular Sciences, vol. 22, no. 5, 2761, 2021.
3. O. V. Forlenza, M. Radanovic, L. L. Talib, I. Aprahamian, B. S. Diniz, H. Zetterberg, and W. F. Gattaz, “Cerebrospinal fluid biomarkers in Alzheimer’s disease: diagnostic accuracy and prediction of dementia,” Alzheimer’s & Dementia: DADM, vol. 1, no. 4, pp. 455–463, 2015.
4. S. Sharma, K. Guleria, S. Tiwari, and S. Kumar, “A deep learning-based convolutional neural network model with VGG16 feature extractor for the detection of Alzheimer disease using MRI scans,” Measurement: Sensors, vol. 24, 100506, 2022.
5. A. W. Salehi, P. Baglat, B. B. Sharma, G. Gupta, and A. Upadhya, “A CNN model: earlier diagnosis and classification of Alzheimer disease using MRI,” in Proc. Int. Conf. Smart Electronics and Communication (ICOSEC), 2020, pp. 156–161.
6. M. M. S. Fareed, S. Zikria, G. Ahmed, S. Mahmood, M. Aslam, S. F. Jillani, A. Moustafa, and M. Asad, “ADD-Net: an effective deep learning model for early detection of Alzheimer disease in MRI scans,” IEEE Access, vol. 10, pp. 96930–96951, 2022.
7. A. Puente-Castro, E. Fernandez-Blanco, A. Pazos, and C. R. Munteanu, “Automatic assessment of Alzheimer’s disease diagnosis based on deep learning techniques,” Computers in Biology and Medicine, vol. 120, 103764, 2020.
8. R. Singh, C. Prabha, H. M. Dixit, and S. Kumari, “Alzheimer disease detection using deep learning,” in Proc. Int. Conf. Self Sustainable Artificial Intelligence Systems (ICSSAS), 2023, pp. 1–6.
9. C. M. Chabib, L. J. Hadjileontiadis, and A. Al Shehhi, “DeepCurvMRI: deep convolutional curvelet transform-based MRI approach for early detection of Alzheimer’s disease,” IEEE Access, vol. 11, pp. 44650–44659, 2023.
10. N. Hassan, A. S. M. Miah, K. Suzuki, Y. Okuyama, and J. Shin, “Stacked CNN-based multichannel attention networks for Alzheimer disease detection,” Scientific Reports, vol. 15, no. 1, 5815, 2025.
11. M. Mujahid, A. Rehman, T. Alam, F. S. Alamri, S. M. Fati, and T. Saba, “An efficient ensemble approach for Alzheimer’s disease detection using an adaptive synthetic technique and deep learning,” Diagnostics, vol. 13, no. 15, 2489, 2023.
12. M. U. Ali, S. J. Hussain, M. Khalid, M. Farrash, H. F. M. Lahza, and A. Zafar, “MRI-driven Alzheimer’s disease diagnosis using deep network fusion and optimal selection of feature,” Bioengineering, vol. 11, no. 11, 1076, 2024.
13. Z. Zhao, P. S. Q. Yeoh, X. Zuo, J. H. Chuah, C. O. Chow, X. Wu, and K. W. Lai, “Vision transformer-equipped convolutional neural networks for automated Alzheimer’s disease diagnosis using 3D MRI scans,” Frontiers in Neurology, vol. 15, 1490829, 2024.
14. H. A. Raza, S. U. Ansari, K. Javed, et al., “A proficient approach for the classification of Alzheimer’s disease using a hybridization of machine learning and deep learning,” Scientific Reports, vol. 14, 30925, 2024.
15. S. Mohsen, “Alzheimer’s disease detection using deep learning and machine learning: a review,” Artificial Intelligence Review, vol. 58, 262, 2025.
16. G. Battineni, M. A. Hossain, N. Chintalapudi, E. Traini, V. R. Dhulipalla, M. Ramasamy, and F. Amenta, “Improved Alzheimer’s disease detection by MRI using multimodal machine learning algorithms,” Diagnostics, vol. 11, no. 11, 2103, 2021.
17. [C. Kavitha, V. Mani, S. R. Srividhya, O. I. Khalaf, and C. A. Tavera Romero, “Early-stage Alzheimer’s disease prediction using machine learning models,” Frontiers in Public Health, vol. 10, 853294, 2022.
18. S. Joshi, G. G. V. Simha, D. P. Shenoy, K. R. Venugopal, and L. M. Patnaik, “Classification and treatment of different stages of Alzheimer’s disease using various machine learning methods,” International Journal of Bioinformatics Research, vol. 2, no. 1, 2010.
19. A. Khan and S. Zubair, “A machine learning-based robust approach to identify dementia progression employing dimensionality reduction in cross-sectional MRI data,” in Proc. IEEE Conf., 2020.
20. D. G. Olle Olle, J. Zoobo Bisse, and G. Abessolo Alo’o, “Application and comparison of K-means and PCA based segmentation models for Alzheimer disease detection using MRI,” Discover Artificial Intelligence, vol. 4, no. 1, 11, 2024.
21. K. M. M. Uddin, M. J. Alam, M. A. Uddin, and S. Aryal, “A novel approach utilizing machine learning for the early diagnosis of Alzheimer’s disease,” Biomedical Materials & Devices, vol. 1, no. 2, pp. 882–898, 2023.
