  Classifying 3D MRI images for early detection of Alzheimer's disease is a
critical task in medical imaging. Traditional approaches using Convolutional
Neural Networks (CNNs) and Transformers face significant challenges in this
domain. CNNs, while effective in capturing local spatial features, struggle
with long-range dependencies and often require extensive computational
resources for high-resolution 3D data. Transformers, on the other hand, excel
in capturing global context but suffer from quadratic complexity in inference
time and require substantial memory, making them less efficient for large-scale
3D MRI data. To address these limitations, we propose the use of Vision Mamba,
an advanced model based on State Space Models (SSMs), for the classification of
3D MRI images to detect Alzheimer's disease. Vision Mamba leverages dynamic
state representations and the selective scan algorithm, allowing it to
efficiently capture and retain important spatial information across 3D volumes.
By dynamically adjusting state transitions based on input features, Vision
Mamba can selectively retain relevant information, leading to more accurate and
computationally efficient processing of 3D MRI data. Our approach combines the
parallelizable nature of convolutional operations during training with the
efficient, recurrent processing of states during inference. This architecture
not only improves computational efficiency but also enhances the model's
ability to handle long-range dependencies within 3D medical images.
Experimental results demonstrate that Vision Mamba outperforms traditional CNN
and Transformer models accuracy, making it a promising tool for the early
detection of Alzheimer's disease using 3D MRI data.
