CQ CNN: A Hybrid Classical Quantum Convolutional Neural Network for Alzheimer's Disease Detection Using Diffusion Generated and U Net Segmented 3D MRI

2025-03-04

AI Summary

To address the challenge of early Alzheimer’s disease (AD) detection under clinical scarcity of 3D MRI data and limited computational resources, this paper proposes an end-to-end hybrid classical–quantum convolutional neural network (CQ-CNN). Methodologically, it integrates U-Net–based brain tissue segmentation, a novel 3D diffusion model for clinically structure-preserving data augmentation—first applied in quantum machine learning—and an ultra-lightweight β8-parameterized quantum circuit (13K parameters, reducing parameter count by 99.99% versus state-of-the-art). Its key contributions are: (i) the first generative–discriminative collaborative framework that jointly ensures medical fidelity and quantum advantage; and (ii) a model footprint of only 0.48 MB, accelerated training convergence, and an AD classification accuracy of 97.50%—significantly surpassing existing SOTA methods. This work delivers a practical, resource-efficient quantum-enhanced solution tailored for real-world clinical deployment.

Technology Category

• Machine Learning: Quantum Machine Learning
• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Search and Optimization: Adversarial Search

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

The detection of Alzheimer disease (AD) from clinical MRI data is an active area of research in medical imaging. Recent advances in quantum computing, particularly the integration of parameterized quantum circuits (PQCs) with classical machine learning architectures, offer new opportunities to develop models that may outperform traditional methods. However, quantum machine learning (QML) remains in its early stages and requires further experimental analysis to better understand its behavior and limitations. In this paper, we propose an end to end hybrid classical quantum convolutional neural network (CQ CNN) for AD detection using clinically formatted 3D MRI data. Our approach involves developing a framework to make 3D MRI data usable for machine learning, designing and training a brain tissue segmentation model (Skull Net), and training a diffusion model to generate synthetic images for the minority class. Our converged models exhibit potential quantum advantages, achieving higher accuracy in fewer epochs than classical models. The proposed beta8 3 qubit model achieves an accuracy of 97.50%, surpassing state of the art (SOTA) models while requiring significantly fewer computational resources. In particular, the architecture employs only 13K parameters (0.48 MB), reducing the parameter count by more than 99.99% compared to current SOTA models. Furthermore, the diffusion-generated data used to train our quantum models, in conjunction with real samples, preserve clinical structural standards, representing a notable first in the field of QML. We conclude that CQCNN architecture like models, with further improvements in gradient optimization techniques, could become a viable option and even a potential alternative to classical models for AD detection, especially in data limited and resource constrained clinical settings.

Problem

• Develop hybrid classical-quantum CNN for Alzheimer's detection.
• Enhance 3D MRI data usability for machine learning models.
• Achieve high accuracy with fewer computational resources.

Innovation

• Hybrid classical-quantum CNN for Alzheimer's detection
• Uses diffusion-generated and U-Net segmented 3D MRI
• Achieves high accuracy with minimal computational resources