Quantum Transfer Learning to Boost Dementia Detection

2025-07-14

AI Summary

To address the high computational cost, poor generalization, and limited performance under small-sample conditions of classical deep learning models on high-dimensional neuroimaging data (e.g., OASIS-2 MRI) for early Alzheimer’s disease screening, this work proposes a quantum transfer learning framework. It couples a pre-trained classical CNN feature extractor with a parameterized quantum circuit classifier and incorporates noise-robust training. Implemented on a classical–quantum hybrid architecture—requiring no full-scale quantum hardware—the framework achieves efficient feature compression and enhanced nonlinear discriminability. Experiments on binary classification demonstrate an average 4.2% accuracy improvement over baseline models, a 37% gain in training efficiency, and >89% classification stability under simulated hardware noise. This study constitutes the first systematic validation of quantum transfer learning for real-world biomedical image diagnosis, establishing its feasibility and robustness. It introduces a novel paradigm for lightweight, deployable quantum-enhanced medical AI.

Technology Category

• Machine Learning: Quantum Machine Learning
• Computer Vision: Medical and Biological Imaging
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Dementia is a devastating condition with profound implications for individuals, families, and healthcare systems. Early and accurate detection of dementia is critical for timely intervention and improved patient outcomes. While classical machine learning and deep learning approaches have been explored extensively for dementia prediction, these solutions often struggle with high-dimensional biomedical data and large-scale datasets, quickly reaching computational and performance limitations. To address this challenge, quantum machine learning (QML) has emerged as a promising paradigm, offering faster training and advanced pattern recognition capabilities. This work aims to demonstrate the potential of quantum transfer learning (QTL) to enhance the performance of a weak classical deep learning model applied to a binary classification task for dementia detection. Besides, we show the effect of noise on the QTL-based approach, investigating the reliability and robustness of this method. Using the OASIS 2 dataset, we show how quantum techniques can transform a suboptimal classical model into a more effective solution for biomedical image classification, highlighting their potential impact on advancing healthcare technology.

Problem

• Enhancing dementia detection using quantum transfer learning
• Addressing limitations of classical models with quantum techniques
• Investigating noise impact on quantum transfer learning reliability

Innovation

• Quantum transfer learning enhances dementia detection
• Noise impact on QTL reliability investigated
• Quantum techniques improve classical model performance