A Novel Transformer-Based Multimodal Fusion Framework for Early Alzheimer's Detection: A Systematic Review and Dataset-Aware Research Agenda

Multimodal transformer fusion offers a powerful route toward early Alzheimer's detection because it can connect brain structure, metabolism, cognition, temporal signals, and clinical history. The real scientific value, however, depends on reproducible datasets, leakage-free evaluation, calibrated risk reporting, explainability, and careful clinical framing. This paper provided an IEEE-style systematic review draft with a 3D conceptual architecture, tables, graphs, dataset map, fusion taxonomy, and quality checklist. It is suitable for further development into a review manuscript or an empirical paper after a final database search and DOI validation. A publishable Q1-level version should replace seed-set counts with exported search logs, include a complete screening appendix, and verify every DOI before submission.

[1] Y. Pusparani et al., "Diagnosis of Alzheimer's Disease Using Convolutional Neural Network With Select Slices by Landmark on Hippocampus in MRI Images," IEEE Access, vol. 11, pp. 61688-61697, 2023, doi: 10.1109/ACCESS.2023.3285115. [2] 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, doi: 10.1109/ACCESS.2023.3272482. [3] F. U. R. Faisal and G.-R. Kwon, "Automated Detection of Alzheimer's Disease and Mild Cognitive Impairment Using Whole Brain MRI," IEEE Access, vol. 10, pp. 65055-65066, 2022, doi: 10.1109/ACCESS.2022.3180073. [4] T. Habuza, N. Zaki, E. Mohamed, and Y. Statsenko, "Deviation From Model of Normal Aging in Alzheimer's Disease: Application of Deep Learning to Structural MRI Data and Cognitive Tests," IEEE Access, vol. 10, pp. 53234-53249, 2022, doi: 10.1109/ACCESS.2022.3174601. [5] M. S. Fareed et al., "ADD-Net: An Effective Deep Learning Model for Early Detection of Alzheimer Disease in MRI Scans," IEEE Access, 2022, doi: 10.1109/ACCESS.2022.3204395. [6] A. Gamal, M. Elattar, and S. Selim, "Automatic Early Diagnosis of Alzheimer's Disease Using 3D Deep Ensemble Approach," IEEE Access, vol. 10, pp. 115974-115987, 2022, doi: 10.1109/ACCESS.2022.3218621. [7] M. K. Keles and U. Kilic, "Classification of Brain Volumetric Data to Determine Alzheimer's Disease Using Artificial Bee Colony Algorithm as Feature Selector," IEEE Access, vol. 10, pp. 82989-83001, 2022, doi: 10.1109/ACCESS.2022.3196649. [8] F. M. J. M. Shamrat et al., "AlzheimerNet: An Effective Deep Learning Based Proposition for Alzheimer's Disease Stages Classification From Functional Brain Changes in Magnetic Resonance Images," IEEE Access, vol. 11, pp. 16376-16395, 2023, doi: 10.1109/ACCESS.2023.3244952. [9] S. Murugan et al., "DEMNET: A Deep Learning Model for Early Diagnosis of Alzheimer Diseases and Dementia From MR Images," IEEE Access, vol. 9, pp. 90319-90329, 2021, doi: 10.1109/ACCESS.2021.3090474. [10] B. S. Rao and M. Aparna, "A Review on Alzheimer's Disease Through Analysis of MRI Images Using Deep Learning Techniques," IEEE Access, vol. 11, pp. 71542-71556, 2023, doi: 10.1109/ACCESS.2023.3294981.