# Dynamic Image for 3D MRI Image Alzheimer's Disease Classification

arXiv: https://arxiv.org/abs/2012.00119 · HTML: https://mlsanity.com/paper/2012.00119/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2020.

## Built on by (most cited first, 5 of 5)

- [Recent Progress in Transformer-based Medical Image Analysis](https://mlsanity.com/paper/2208.06643/) (2022)
- [NeuroMoE: A Transformer-Based Mixture-of-Experts Framework for Multi-Modal Neurological Disorder Classification](https://mlsanity.com/paper/2506.14970/) (2025)
- [DCL-SE: Dynamic Curriculum Learning for Spatiotemporal Encoding of Brain Imaging](https://mlsanity.com/paper/2511.15151/) (2025)
- [OpenAPMax: Abnormal Patterns-based Model for Real-World Alzheimer's Disease Diagnosis](https://mlsanity.com/paper/2307.00936/) (2023)
- [Attention-based Efficient Classification for 3D MRI Image of Alzheimer's Disease](https://mlsanity.com/paper/2401.14130/) (2024)

## Abstract (arXiv)

We propose to apply a 2D CNN architecture to 3D MRI image Alzheimer's disease classification. Training a 3D convolutional neural network (CNN) is time-consuming and computationally expensive. We make use of approximate rank pooling to transform the 3D MRI image volume into a 2D image to use as input to a 2D CNN. We show our proposed CNN model achieves 9.5% better Alzheimer's disease classification accuracy than the baseline 3D models. We also show that our method allows for efficient training, requiring only 20% of the training time compared to 3D CNN models. The code is available online: https://github.com/UkyVision/alzheimer-project.