# Preclinical Stage Alzheimer's Disease Detection Using Magnetic Resonance Image Scans

arXiv: https://arxiv.org/abs/2011.14139 · HTML: https://mlsanity.com/paper/2011.14139/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2020.

## Built on by (most cited first, 12 of 12)

- [Transformers in Medical Image Analysis: A Review](https://mlsanity.com/paper/2202.12165/) (2022)
- [UniBrain: Universal Brain MRI Diagnosis with Hierarchical Knowledge-enhanced Pre-training](https://mlsanity.com/paper/2309.06828/) (2023)
- [Diagnosing Alzheimer's Disease using Early-Late Multimodal Data Fusion with Jacobian Maps](https://mlsanity.com/paper/2310.16936/) (2023)
- [AXIAL: Attention-based eXplainability for Interpretable Alzheimer's Localized Diagnosis using 2D CNNs on 3D MRI brain scans](https://mlsanity.com/paper/2407.02418/) (2024)
- [Unmasking Dementia Detection by Masking Input Gradients: A JSM Approach to Model Interpretability and Precision](https://mlsanity.com/paper/2402.16008/) (2024)
- [Assessing the significance of longitudinal data in Alzheimer's Disease forecasting](https://mlsanity.com/paper/2405.17352/) (2024)
- [Flexible Multimodal Neuroimaging Fusion for Alzheimer's Disease Progression Prediction](https://mlsanity.com/paper/2509.12234/) (2025)
- [Parcel2Progression: An Anatomy-aware Longitudinal Framework for Alzheimer's Disease Diagnosis](https://mlsanity.com/paper/2608.08753/) (2026)
- [Multi-task Collaborative Pre-training and Individual-adaptive-tokens Fine-tuning: A Unified Framework for Brain Representation Learning](https://mlsanity.com/paper/2306.11378/) (2023)
- [Predicting Outcomes in Long COVID Patients with Spatiotemporal Attention](https://mlsanity.com/paper/2307.04770/) (2023)
- [Efficient Brain Imaging Analysis for Alzheimer's and Dementia Detection Using Convolution-Derivative Operations](https://mlsanity.com/paper/2411.13490/) (2024)
- [Multiple Inputs and Mixwd data for Alzheimer's Disease Classification Based on 3D Vision Transformer](https://mlsanity.com/paper/2603.00545/) (2026)

## Abstract (arXiv)

Alzheimer's disease is one of the diseases that mostly affects older people without being a part of aging. The most common symptoms include problems with communicating and abstract thinking, as well as disorientation. It is important to detect Alzheimer's disease in early stages so that cognitive functioning would be improved by medication and training. In this paper, we propose two attention model networks for detecting Alzheimer's disease from MRI images to help early detection efforts at the preclinical stage. We also compare the performance of these two attention network models with a baseline model. Recently available OASIS-3 Longitudinal Neuroimaging, Clinical, and Cognitive Dataset is used to train, evaluate and compare our models. The novelty of this research resides in the fact that we aim to detect Alzheimer's disease when all the parameters, physical assessments, and clinical data state that the patient is healthy and showing no symptoms