Hybrid Topological and Deep Feature Fusion for Accurate MRI-Based Alzheimer's Disease Severity Classification

TL;DR

This paper introduces a hybrid deep learning framework combining Topological Data Analysis and DenseNet121 to improve the accuracy of MRI-based Alzheimer's disease classification, achieving near-perfect results on the OASIS dataset.

Contribution

The novel integration of topological features with deep learning for Alzheimer's classification enhances accuracy beyond existing methods.

Findings

Achieved 99.93% accuracy and 100% AUC on OASIS dataset.

Topological features complement deep learning for better class separation.

Outperforms state-of-the-art CNN and ensemble models.

Abstract

Early and accurate diagnosis of Alzheimer's disease (AD) remains a critical challenge in neuroimaging-based clinical decision support systems. In this work, we propose a novel hybrid deep learning framework that integrates Topological Data Analysis (TDA) with a DenseNet121 backbone for four-class Alzheimer's disease classification using structural MRI data from the OASIS dataset. TDA is employed to capture complementary topological characteristics of brain structures that are often overlooked by conventional neural networks, while DenseNet121 efficiently learns hierarchical spatial features from MRI slices. The extracted deep and topological features are fused to enhance class separability across the four AD stages. Extensive experiments conducted on the OASIS-1 Kaggle MRI dataset demonstrate that the proposed TDA+DenseNet121 model significantly outperforms existing state-of-the-art approaches. The model achieves an accuracy of 99.93% and an AUC of 100%, surpassing recently published CNN-based, transfer learning, ensemble, and multi-scale architectures. These results confirm the effectiveness of incorporating topological insights into deep learning pipelines and highlight the potential of the proposed framework as a robust and highly accurate tool for automated Alzheimer's disease diagnosis.