Comprehensive Summary
This paper introduces CQ-CNN, a new hybrid model that combines classical and quantum computing to detect Alzheimer’s disease from 3D MRI brain scans. The researchers designed a framework to convert 3D MRI data into 2D images and built a lightweight convolutional neural network that includes a quantum circuit layer. Using the OASIS-2 dataset, the model was trained to distinguish between patients with moderate dementia and those without dementia. Despite some training challenges, the CQ-CNN achieved up to 97.5% accuracy with only 13,700 parameters, which is fewer than standard deep learning models like ResNet or AlexNet. This showed that hybrid quantum models can reach comparable or better accuracy while using significantly fewer resources.
Outcomes and Implications
The study highlights the promise of quantum-enhanced AI in medical imaging. A system like CQ-CNN could help clinicians detect Alzheimer’s disease earlier and more efficiently, especially in resource-limited settings where heavy computational models are difficult to use. By offering faster and more lightweight image analysis, this approach could improve diagnosis and support more personalized patient care. Although the technology is still in early stages, continued improvements in quantum optimization and hardware could make such models clinically viable in the near future.