Multimodal Deep Learning (DL) for Early Alzheimer's Disease (AD) Detection: Leveraging MRI and Clinical Data
DOI:
https://doi.org/10.58564/IJSER.4.3.2025.322Keywords:
Alzheimer’s Disease; MRI; clinical data; multimodal deep learning; attention mechanismAbstract
Alzheimer’s Disease is a degenerative brain disorder that progressively impairs cognitive functions, particularly memory, and poses a significant burden on individuals and healthcare systems worldwide. Timely and accurate diagnosis of AD in its early stages is crucial to enable effective interventions and support patient care. Traditional diagnostic approaches often rely on either structural brain imaging or clinical evaluation, yet using one modality in isolation limits the ability to capture the complexity of the disease. This study introduces a multimodal deep learning framework designed to integrate structural Magnetic Resonance Imaging (MRI) with comprehensive clinical data for early detection of AD. The proposed system employs a three-dimensional convolutional neural network (3D-CNN) to analyze volumetric MRI scans and a multi-layer perceptron (MLP) to process structured clinical features. To enhance representational learning, the model applies an attention-based fusion strategy, including Transformer mechanisms, which enable it to focus on the most relevant modality-specific features. Furthermore, an ensemble learning approach combines the predictions of the individual modalities and the multimodal fusion branch, significantly improving the overall diagnostic performance. While the framework's proof-of-concept validation was conducted on a simulated dataset, the results demonstrate a high degree of accuracy and offer a strong basis for its application to real-world clinical data. We demonstrate that our ensemble model achieves a superior accuracy of 97.0% and an AUC of 0.985, outperforming unimodal and non-attentive multimodal baselines. The framework's explain ability, highlighted by Grad-CAM and SHAP, offers valuable insights into the model's decision-making process, a critical step towards its clinical acceptance.
References
[1] A. Juganavar, A. Joshi, and T. Shegekar, "Navigating early Alzheimer's diagnosis: A comprehensive review of diagnostic innovations," Cureus, vol. 15, 2023, Art. no. e44786. Available: https://doi.org/10.7759/cureus.44786.
[2] C. Saraiva, C. Praça, R. Ferreira, T. Santos, L. Ferreira, and L. Bernardino, “Nanoparticle-mediated brain drug delivery: Overcoming blood–brain barrier to treat neurodegenerative diseases,” Journal of Controlled Release, vol. 235, pp. 34–47, Aug. 2016, doi: 10.1016/J.JCONREL.2016.05.044.
[3] M. R. Chorawala, A. C. Shah, A. J. Pandya, N. R. Kothari, and B. G. Prajapati, “Symptoms and conventional treatments of Alzheimer’s disease,” in Alzheimer’s Disease and Advanced Drug Delivery Strategies, Elsevier, 2024, pp. 213–234. doi: 10.1016/B978-0-443-13205-6.00009-1.
[4] B. Dubois, G. Picard, and M. Sarazin, “Early detection of Alzheimer’s disease: new diagnostic criteria,” Dialogues Clin Neurosci, vol. 11(2), no. 2, pp. 135–139, 2009, doi: 10.31887/DCNS.2009.11.2/BDUBOIS.
[5] R. C. Petersen, G. E. Smith, S. C. Waring, R. J. Ivnik, E. G. Tangalos, and E. Kokmen, “Mild Cognitive Impairment: Clinical Characterization and Outcome,” Arch Neurol, vol. 56(3), no. 3, pp. 303–308, Mar. 1999, doi: 10.1001/ARCHNEUR.56.3.303.
[6] Ahmed Jasim, A., Alwindawi, H., & Rafea Hazim, L. (2025). Empowering Diagnostics: An Ensemble Machine Learning Model for Early Liver Disease Detection. Al-Iraqia Journal for Scientific Engineering Research, 4(2), 13–19. https://doi.org/10.58564/IJSER.4.2.2025.314
[7] A. J. Mitchell and M. Shiri-Feshki, “Temporal trends in the long term risk of progression of mild cognitive impairment: a pooled analysis,” J Neurol Neurosurg Psychiatry, vol. 79(12), no. 12, pp. 1386–1391, Dec. 2008, doi: 10.1136/JNNP.2007.142679.
[8] M. Ahmed and I. Husien, "Predicting Heart Disease Using Hybrid Machine Learning Techniques: A Review," J. Robot. Control, vol. 5, no. 3, pp. 884–892, 2024.
[9] D. H. Mustafa and I. M. Husien, "Enhancing Botnet Detection Using Adaptive DBSCAN with Grey Wolf Optimizer," Int. J. Intell. Eng. Syst., vol. 16, no. 4, 2023.
[10] M. Golovanevsky "Multimodal Deep Learning Applications for Alzheimer's Diagnosis." Journal of the American Medical Informatics Association, Volume 29, Issue 12, December 2022, Pages 2014–2022, https://doi.org/10.1093/jamia/ocac168.
[11] Saad Jameel, G., N. Al-Turfi , M., Salih Al-Obaidi, W., & H. Hamoudi, G. (2024). Portable ECG Device Based on Deep learning and Raspberry PI 4. Al-Iraqia Journal for Scientific Engineering Research, 3(2), 77–82. https://doi.org/10.58564/IJSER.3.2.2024.181
[12] Jahan S, Abu Taher K, Kaiser MS, Mahmud M, Rahman MS, Hosen ASMS, Ra IH. Explainable AI-based Alzheimer's prediction and management using multimodal data. PLoS One. 2023 Nov 16;18(11):e0294253. doi: 10.1371/journal.pone.0294253.
[13] A. Abrol, M. Bhattarai, A. Fedorov, Y. Du, S. Plis, and V. Calhoun, “Deep residual learning for neuroimaging: An application to predict progression to Alzheimer’s disease,” J Neurosci Methods, vol. 339, p. 108701, Jun. 2020, doi: 10.1016/J.JNEUMETH.2020.108701.
[14] H. Guo and Y. Zhang, “Resting State fMRI and Improved Deep Learning Algorithm for Earlier Detection of Alzheimer’s Disease,” IEEE Access, vol. 8, pp. 115383–115392, 2020, doi: 10.1109/ACCESS.2020.3003424.
[15] S. S. Kundaram and K. C. Pathak, “Deep Learning-Based Alzheimer Disease Detection,” Lecture Notes in Electrical Engineering, vol. 673, pp. 587–597, 2021, doi: 10.1007/978-981-15-5546-6_50/COVER.
[16] E. M. Mohammed, A. M. Fakhrudeen, and O. Y. Alani, "A Systematic Literature Review on Alzheimer's Disease Detection Using Deep Learning," Informatics Med. Unlocked, vol. 50, 101551, 2024.
[17] E. Pellegrini et al., “Machine learning of neuroimaging for assisted diagnosis of cognitive impairment and dementia: A systematic review,” Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring, vol. 10, pp. 519– 535, Jan. 2018, doi: 10.1016/J.DADM.2018.07.004.
[18] L. Zhang, M. Wang, M. Liu, and D. Zhang, “A Survey on Deep Learning for Neuroimaging-Based Brain Disorder Analysis,” Front Neurosci, vol. 14, p. 560709, Oct. 2020, doi: 10.3389/FNINS.2020.00779/BIBTEX.
[19] N. Burgos and O. Colliot, “Machine learning for classification and prediction of brain diseases: recent advances and upcoming challenges,” Curr Opin Neurol, vol. 33(4), no. 4, pp. 439–450, Aug. 2020, doi: 10.1097/WCO.0000000000000838.
[20] S. Al-Shoukry, T. H. Rassem, and N. M. Makbol, “Alzheimer’s diseases detection by using deep learning algorithms: A mini-review,” IEEE Access, vol. 8, pp. 77131–77141, 2020, doi: 10.1109/ACCESS.2020.2989396.
[21] S. Balne and A. Elumalai, “Machine learning and deep learning algorithms used to diagnosis of Alzheimer’s: Review,” Mater Today Proc, vol. 47(15), pp. 5151–5156, Jan. 2021, doi: 10.1016/J.MATPR.2021.05.499.
[22] A. Shetty, D. Mehta, P. Rane, and S. N. Dodani, “Detection and Prediction of Alzheimer’s disease using Deep learning: A review,” in 2021 International Conference on Nascent Technologies in Engineering, ICNET 2021 - Proceedings, Institute of Electrical and Electronics Engineers Inc., Jan. 2021, pp. 1–7. doi: 10.1109/ICNTE51185.2021.9487587.
[23] S. Gao and D. Lima, “A review of the application of deep learning in the detection of Alzheimer’s disease,” International Journal of Cognitive Computing in Engineering, vol. 3, pp. 1–8, Jun. 2022, doi: 10.1016/J.IJCCE.2021.12.002.
[24] V. Patil, M. Madgi, and A. Kiran, “Early prediction of Alzheimer’s disease using convolutional neural network: a review,” Egyptian Journal of Neurology, Psychiatry and Neurosurgery, vol. 58(1), no. 1, pp. 1–10, Dec. 2022, doi: 10.1186/S41983-022-00571-W/FIGURES/3.
[25] D. A. Arafa, H. E. D. Moustafa, A. M. T. Ali-Eldin, and H. A. Ali, “Early detection of Alzheimer’s disease based on the state-of-the-art deep learning approach: a comprehensive survey,” Multimed Tools Appl, vol. 81(17), no. 17, pp. 23735–23776, Jul. 2022, doi: 10.1007/s11042-022-11925-0.
[26] P. S. Sisodia, G. K. Ameta, Y. Kumar, and N. Chaplot, “A Review of Deep Transfer Learning Approaches for Class-Wise Prediction of Alzheimer’s Disease Using MRI Images,” Archives of Computational Methods in Engineering, vol. 30(4), no. 4, pp. 2409–2429, May 2023, doi: 10.1007/S11831-022-09870-0/METRICS.
[27] A. Deep Arya et al., “A Systematic Review on Machine Learning and Deep Learning Techniques in the Effective Diagnosis of Alzheimer’s Disease,” Brain Inform, vol. 10(17), Sep. 2023, doi: 10.21203/RS.3.RS-2028945/V1.
[28] M. Nawaz, S. Saleem, M. Masood, J. Rashid, and T. Nazir, “COVID-ECG- RSNet: COVID-19 classification from ECG images using swish-based improved ResNet model,” Biomed Signal Process Control, vol. 89, p. 105801, Mar. 2024, doi: 10.1016/J.BSPC.2023.105801.
[29] I. Beheshti, M. A. Ganaie, V. Paliwal, A. Rastogi, I. Razzak, and M. Tanveer, “Predicting Brain Age Using Machine Learning Algorithms: A Comprehensive Evaluation,” IEEE J Biomed Health Inform, vol. 26(4), no. 4,pp. 1432–1440, Apr. 2022, doi: 10.1109/JBHI.2021.3083187.
[30] A. Barragán-Montero et al., “Artificial intelligence and machine learning for medical imaging: A technology review,” Physica Medica, vol. 83, pp. 242– 256, Mar. 2021, doi: 10.1016/J.EJMP.2021.04.016.
[31] M. M. Malik, “A Hierarchy of Limitations in Machine Learning,” arXiv preprint arXiv:2002.05193, Feb. 2020, doi: 10.48550/arXiv 2002.05193.
[32] M. M. Ahsan, S. A. Luna, and Z. Siddique, “Machine-Learning-Based Disease Diagnosis: A Comprehensive Review,” Healthcare, vol. 10(3), no. 3, p. 541, Mar. 2022, doi: 10.3390/HEALTHCARE10030541.
[33] Goodfellow Ian, Bengio Yoshua, and Courville Aaron, Deep Learning. MIT Press, 2016. Accessed: Jun. 21, 2024. [Online]. Available: https://www.deeplearningbook.org/
[34] D. R. Sarvamangala and R. V. Kulkarni, “Convolutional neural networks in medical image understanding: a survey,” Evol Intell, vol. 15(1), no. 1, Mar. 2022, doi: 10.1007/s12065-020-00540-3.
M. A. Ebrahimighahnavieh, S. Luo, and R. Chiong, “Deep learning to detect Alzheimer’s disease from neuroimaging: A systematic literature review,” Comput Methods Programs Biomed, vol. 187, p. 105242, Apr. 2020, doi: 10.1016/J.CMPB.2019.105242.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Idress Husien, Mohammed Ahmed, Mete Ozbaltan, Mohammad Sarfraz
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
