A new study combines 3D MRI and PET data using advanced fusion strategies including GMU and gated self-attention, along with a sparsely gated MoE classifier. Results show GMU achieves 80.46% accuracy on NC vs. MCI and 95.47% on NC vs. AD, with gated self-attention reaching 82.08% on MCI vs. AD. Ablations confirm the MoE significantly improves performance, highlighting the importance of input-adaptive multimodal modeling for accurate Alzheimer's diagnosis.