3D-TDA -- Topological feature extraction from 3D images for Alzheimer's disease classification

TL;DR

This paper introduces a novel topological feature extraction method using persistent homology for Alzheimer's disease classification from 3D MRI images, achieving high accuracy with efficient computation and minimal preprocessing.

Contribution

The study presents a new approach combining topological features with machine learning, outperforming deep learning models without extensive data preprocessing.

Findings

Achieved 97.43% accuracy in binary classification

Achieved 95.47% accuracy in three-class classification

Model requires no data augmentation or extensive preprocessing

Abstract

Now that disease-modifying therapies for Alzheimer disease have been approved by regulatory agencies, the early, objective, and accurate clinical diagnosis of AD based on the lowest-cost measurement modalities possible has become an increasingly urgent need. In this study, we propose a novel feature extraction method using persistent homology to analyze structural MRI of the brain. This approach converts topological features into powerful feature vectors through Betti functions. By integrating these feature vectors with a simple machine learning model like XGBoost, we achieve a computationally efficient machine learning model. Our model outperforms state-of-the-art deep learning models in both binary and three-class classification tasks for ADNI 3D MRI disease diagnosis. Using 10-fold cross-validation, our model achieved an average accuracy of 97.43 percent and sensitivity of 99.09 percent for binary classification. For three-class classification, it achieved an average accuracy of 95.47 percent and sensitivity of 94.98 percent. Unlike many deep learning models, our approach does not require data augmentation or extensive preprocessing, making it particularly suitable for smaller datasets. Topological features differ significantly from those commonly extracted using convolutional filters and other deep learning machinery. Because it provides an entirely different type of information from machine learning models, it has the potential to combine topological features with other models later on.