# Attention-based Efficient Classification for 3D MRI Image of Alzheimer's Disease

arXiv: https://arxiv.org/abs/2401.14130 · HTML: https://mlsanity.com/paper/2401.14130/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 27 references that are in the map.
- First posted on arXiv 25 Jan 2024.

## Builds on (most cited first, 5 of 5)

- [Residual and Plain Convolutional Neural Networks for 3D Brain MRI Classification](https://mlsanity.com/paper/1701.06643/) (2017)
- [End-To-End Alzheimer's Disease Diagnosis and Biomarker Identification](https://mlsanity.com/paper/1810.00523/) (2018)
- [Binary Classification of Alzheimer Disease using sMRI Imaging modality and Deep Learning](https://mlsanity.com/paper/1809.06209/) (2018)
- [Forecasting the Progression of Alzheimer's Disease Using Neural Networks and a Novel Pre-Processing Algorithm](https://mlsanity.com/paper/1903.07510/) (2019)
- [Dynamic Image for 3D MRI Image Alzheimer's Disease Classification](https://mlsanity.com/paper/2012.00119/) (2020)

## Abstract (arXiv)

Early diagnosis of Alzheimer Diagnostics (AD) is a challenging task due to its subtle and complex clinical symptoms. Deep learning-assisted medical diagnosis using image recognition techniques has become an important research topic in this field. The features have to accurately capture main variations of anatomical brain structures. However, time-consuming is expensive for feature extraction by deep learning training. This study proposes a novel Alzheimer's disease detection model based on Convolutional Neural Networks. The model utilizes a pre-trained ResNet network as the backbone, incorporating post-fusion algorithm for 3D medical images and attention mechanisms. The experimental results indicate that the employed 2D fusion algorithm effectively improves the model's training expense. And the introduced attention mechanism accurately weights important regions in images, further enhancing the model's diagnostic accuracy.