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

2026-02-01

AI Summary

This study addresses the challenge of insufficient diagnostic accuracy in early-stage Alzheimer’s disease (AD) classification across four MRI-based stages by proposing a novel integration of topological data analysis (TDA) with DenseNet121. The method jointly extracts topological features of brain structure and deep spatial features through a carefully designed feature fusion strategy. Evaluated on the OASIS-1 Kaggle MRI dataset, the approach achieves a classification accuracy of 99.93% and a perfect AUC of 100%, substantially outperforming existing methods based on CNNs, transfer learning, ensemble techniques, and multi-scale architectures. By explicitly incorporating topological information—often overlooked in conventional deep learning frameworks—this work establishes a new paradigm for precise AD staging and demonstrates the potential of hybrid topological–deep learning models in neuroimaging analysis.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Humans and AI: Brain-Sensing and Analysis
• Computer Vision: Generative Adversarial Networks (GANs) for Vision

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Web Mining and Content Analysis: Machine learning and data science for the Web
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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.

Problem

• Alzheimer's Disease
• MRI
• Disease Severity Classification
• Early Diagnosis
• Neuroimaging

Innovation

• Topological Data Analysis
• Deep Feature Fusion
• DenseNet121
• Alzheimer's Disease Classification
• Structural MRI