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

arXiv: https://arxiv.org/abs/2503.02345 · HTML: https://mlsanity.com/paper/2503.02345/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 57 references that are in the map.
- First posted on arXiv 4 Mar 2025.

## Builds on (most cited first, 10 of 10)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Pros and Cons of GAN Evaluation Measures](https://mlsanity.com/paper/1802.03446/) (2018)
- [CondenseNet: An Efficient DenseNet using Learned Group Convolutions](https://mlsanity.com/paper/1711.09224/) (2017)
- [Diffusion Probabilistic Models beat GANs on Medical Images](https://mlsanity.com/paper/2212.07501/) (2022)
- [Melanoma Detection using Adversarial Training and Deep Transfer Learning](https://mlsanity.com/paper/2004.06824/) (2020)
- [Soft-Attention Improves Skin Cancer Classification Performance](https://mlsanity.com/paper/2105.03358/) (2021)
- [MRI-based Alzheimer's disease prediction via distilling the knowledge in multi-modal data](https://mlsanity.com/paper/2104.03618/) (2021)
- [CosSIF: Cosine similarity-based image filtering to overcome low inter-class variation in synthetic medical image datasets](https://mlsanity.com/paper/2307.13842/) (2023)
- [Bridging Classical and Quantum Machine Learning: Knowledge Transfer From Classical to Quantum Neural Networks Using Knowledge Distillation](https://mlsanity.com/paper/2311.13810/) (2023)

## Built on by (most cited first, 1 of 1)

- [Quantum Model Parallelism for MRI-Based Classification of Alzheimer's Disease Stages](https://mlsanity.com/paper/2602.00128/) (2026)

## Abstract (arXiv)

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.