QENNA: A quantum-enhanced neural network for early Alzheimer's detection using magnetic resonance imaging.
Authors
Affiliations (9)
Affiliations (9)
- Faculty of Computer Science, Ubon Ratchathani Rajabhat University, Mueang, Ubon Ratchathani, 34000, Thailand. Electronic address: [email protected].
- Artificial Intelligence Optimization SMART Laboratory, Industrial Engineering Department, Faculty of Engineering, Ubon Ratchathani University, Ubon Ratchathani, 34190, Thailand. Electronic address: [email protected].
- Artificial Intelligence Optimization SMART Laboratory, Industrial Engineering Department, Faculty of Engineering, Ubon Ratchathani University, Ubon Ratchathani, 34190, Thailand. Electronic address: [email protected].
- Artificial Intelligence Optimization SMART Laboratory, Industrial Engineering Department, Faculty of Engineering, Ubon Ratchathani University, Ubon Ratchathani, 34190, Thailand. Electronic address: [email protected].
- Department of Industrial Engineering, Faculty of Engineering, Rajamangala University of Technology Lanna, Chiang Rai, 57120, Thailand. Electronic address: [email protected].
- Department of Computer Engineering and Automation, Kalasin University, Kalasin, 46000, Thailand. Electronic address: [email protected].
- Department of Information Technology, Faculty of Science, Buriram University, Buriram, 31000, Thailand. Electronic address: [email protected].
- Department of Industrial Engineering, Faculty of Engineering and Technology, Rajamangala University of Technology Isan, Nakhon Ratchasima, 30000, Thailand. Electronic address: [email protected].
- Artificial Intelligence Optimization SMART Laboratory, Industrial Engineering Department, Faculty of Engineering, Ubon Ratchathani University, Ubon Ratchathani, 34190, Thailand. Electronic address: [email protected].
Abstract
Early detection of Alzheimer's disease (AD) is essential for effective clinical intervention and disease management. However, conventional Deep Learning (DL) methods face limitations in analyzing complex brain magnetic resonance imaging (MRI), especially when training data are scarce. In this study, we propose a Quantum-Enhanced Neural Network Architecture (QENNA) that integrates quantum convolutional layers with classical deep learning to improve diagnostic accuracy in early AD detection. The model also incorporates quantum data augmentation strategies, including Quantum Generative Adversarial Networks (QGANs) and quantum random walks, to generate high-fidelity synthetic MRI scans and address training data limitations. Experiments on two public MRI datasets demonstrate that QENNA achieves up to 93.0 % accuracy and 96.0 % Area Under the Curve (AUC), outperforming state-of-the-art classical models. Ablation studies confirm that the quantum components substantially enhance performance. These results suggest that quantum-enhanced learning frameworks can significantly advance Artificial Intelligence (AI)-driven diagnostic tools for neurodegenerative disorders and support scalable, early-stage AD screening in clinical practice.