CQ-CNN: A lightweight hybrid classical-quantum convolutional neural network for Alzheimer's disease detection using 3D structural brain MRI.

ABSTRACT: 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 convert

SUBMITTER: Islam M 

PROVIDER: S-EPMC12453261 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature