Islam Mominul, Hasan Mohammad Junayed, Mahdy M R C
Department of Electrical and Computer Engineering, North South University, Dhaka, Bangladesh.
Mahdy Research Academy, Dhaka, Bangladesh.
PLoS One. 2025 Sep 22;20(9):e0331870. doi: 10.1371/journal.pone.0331870. eCollection 2025.
The automatic detection of Alzheimer's disease (AD) using 3D volumetric MRI data is a complex, multi-domain challenge that has traditionally been addressed by training classical convolutional neural networks (CNNs). With the rise of quantum computing and its potential to replace classical systems in the future, there is a growing need to: (i) develop automated systems for AD detection that run on quantum computers, (ii) explore the capabilities of current-generation classical-quantum architectures, and (iii) identify their potential limitations and advantages. To reduce the complexity of multi-domain expertise while addressing the emerging demands of quantum-based automated systems, our contribution in this paper is twofold. First, we introduce a simple preprocessing framework that converts 3D MRI volumetric data into 2D slices. Second, we propose CQ-CNN, a parameterized quantum circuit (PQC)-based lightweight hybrid classical-quantum convolutional neural network that leverages the computational capabilities of both classical and quantum systems. Our experiments on the OASIS-2 dataset reveal a significant limitation in current hybrid classical-quantum architectures, as they face difficulties converging when class images are highly similar, such as between moderate dementia and non-dementia classes of AD, which leads to gradient failure and optimization stagnation. However, when convergence is achieved, the quantum model demonstrates a promising quantum advantage by attaining state-of-the-art accuracy with far fewer parameters than classical models. For instance, our [Formula: see text]-3-qubit model achieves 97.5% accuracy using only 13.7K parameters (0.05 MB), which is 5.67% higher than a classical model with the same parameter count. Nevertheless, our results highlight the need for improved quantum optimization methods to support the practical deployment of hybrid classical-quantum models in AD detection and related medical imaging tasks.
利用3D容积MRI数据自动检测阿尔茨海默病（AD）是一项复杂的多领域挑战，传统上通过训练经典卷积神经网络（CNN）来解决。随着量子计算的兴起及其未来取代经典系统的潜力，越来越需要：（i）开发在量子计算机上运行的AD检测自动化系统，（ii）探索当前一代经典 - 量子架构的能力，以及（iii）识别它们的潜在局限性和优势。为了在满足基于量子的自动化系统新需求的同时降低多领域专业知识的复杂性，我们在本文中的贡献有两个方面。首先，我们引入了一个简单的预处理框架，将3D MRI容积数据转换为2D切片。其次，我们提出了CQ - CNN，这是一种基于参数化量子电路（PQC）的轻量级混合经典 - 量子卷积神经网络，它利用了经典和量子系统的计算能力。我们在OASIS - 2数据集上的实验揭示了当前混合经典 - 量子架构的一个重大局限性，即当类别图像高度相似时，例如在AD的中度痴呆和非痴呆类别之间，它们在收敛时面临困难，这会导致梯度失效和优化停滞。然而，当实现收敛时，量子模型通过使用比经典模型少得多的参数达到了最先进的准确率，从而展示了有前景的量子优势。例如，我们的[公式：见正文] - 3量子比特模型仅使用13.7K个参数（0.05MB）就达到了97.5%的准确率，比具有相同参数数量的经典模型高5.67%。尽管如此，我们的结果强调了需要改进量子优化方法，以支持混合经典 - 量子模型在AD检测和相关医学成像任务中的实际部署。
