# Detection of Alzheimer's Disease Using Graph-Regularized Convolutional Neural Network Based on Structural Similarity Learning of Brain Magnetic Resonance Images

arXiv: https://arxiv.org/abs/2102.13517 · HTML: https://mlsanity.com/paper/2102.13517/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 33 references that are in the map.
- First posted on arXiv 25 Feb 2021.

## Builds on (most cited first, 7 of 7)

- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Adversarial Autoencoders](https://mlsanity.com/paper/1511.05644/) (2015)
- [Towards Alzheimer's Disease Classification through Transfer Learning](https://mlsanity.com/paper/1711.11117/) (2017)
- [Transfer Learning with intelligent training data selection for prediction of Alzheimer's Disease](https://mlsanity.com/paper/1906.01160/) (2019)
- [Large Scale Distributed Semi-Supervised Learning Using Streaming Approximation](https://mlsanity.com/paper/1512.01752/) (2015)
- [Prediction of Progression to Alzheimer's disease with Deep InfoMax](https://mlsanity.com/paper/1904.10931/) (2019)
- [A Review of Artificial Intelligence Technologies for Early Prediction of Alzheimer's Disease](https://mlsanity.com/paper/2101.01781/) (2020)

## Built on by (most cited first, 1 of 1)

- [Hybridized Convolutional Neural Networks and Long Short-Term Memory for Improved Alzheimer's Disease Diagnosis from MRI Scans](https://mlsanity.com/paper/2403.05353/) (2024)

## Abstract (arXiv)

Objective: This paper presents an Alzheimer's disease (AD) detection method based on learning structural similarity between Magnetic Resonance Images (MRIs) and representing this similarity as a graph. Methods: We construct the similarity graph using embedded features of the input image (i.e., Non-Demented (ND), Very Mild Demented (VMD), Mild Demented (MD), and Moderated Demented (MDTD)). We experiment and compare different dimension-reduction and clustering algorithms to construct the best similarity graph to capture the similarity between the same class images using the cosine distance as a similarity measure. We utilize the similarity graph to present (sample) the training data to a convolutional neural network (CNN). We use the similarity graph as a regularizer in the loss function of a CNN model to minimize the distance between the input images and their k-nearest neighbours in the similarity graph while minimizing the categorical cross-entropy loss between the training image predictions and the actual image class labels. Results: We conduct extensive experiments with several pre-trained CNN models and compare the results to other recent methods. Conclusion: Our method achieves superior performance on the testing dataset (accuracy = 0.986, area under receiver operating characteristics curve = 0.998, F1 measure = 0.987). Significance: The classification results show an improvement in the prediction accuracy compared to the other methods. We release all the code used in our experiments to encourage reproducible research in this area