Quantum-enhanced deep learning for Parkinson’s disease classification

Abstract Parkinson’s disease (PD) involves a slowly advancing neurological condition that profoundly impairs motor as well as cognitive abilities, making prompt and precise diagnosis essential for successful treatment. Diagnosing PD using MRI images is difficult because of subtle changes within the brain and imbalanced datasets. While existing methods such as traditional Machine Learning (ML) and Deep Learning (DL) have made progress, they often struggle to capture important features and effectively handle limited data . To resolve these concerns, this work suggests a combined approach starting with generating additional MRI images using a Deep Convolutional Generative Adversarial Network (DCGAN) to balance the dataset. The feature extraction from these images is performed using the InceptionV3 model. These features are then enhanced by a Pyramid Attention Network (PAN), which helps to concentrate on the data’s most pertinent sections. Finally, the enhanced features are classified using a Variational Quantum Classifier (VQC) with amplitude encoding, which leverages quantum-inspired ML techniques to improve classification performance. This pipeline achieved an overall accuracy of 82.04%, outperforming earlier models. The results demonstrate the feasibility of integrating quantum-inspired models within a hybrid framework for PD classification from MRI scans.