Introduction: Parkinson's disease (PD) is a progressive neurodegenerative disorder caused by the loss of dopaminergic neurons in the substantia nigra, presenting with motor and non-motor symptoms in roughly 2-3% of the global population above age 60. Early detection is difficult because symptoms are subtle and often indistinguishable from normal aging. The MDS-UPDRS rating scale, the current clinical standard, is time-intensive, subjective, and requires experienced clinicians. Most computational approaches are unimodal and do not use image and structured clinical data in combination. Methods: We propose a hybrid quantum-classical dual-track multimodal network that classifies PD patients and healthy controls from hand-drawn spiral and meander patterns. The first track, the Topological Visual-Spatial Feature Encoder Network (TVSFE), uses a ghost module-based CNN with Cross-Dimensional Attention Bottleneck (CDAB) blocks incorporating coordinate attention, squeeze-and-excitation, and triplet attention, followed by a quantum variational circuit with amplitude embedding. The second track, the Variational Quantum Feature Mapping Network (VQFMN), encodes structured clinical and demographic data through RY rotation gates and strongly entangling layers. Outputs from both tracks are concatenated and passed through fully connected layers for classification. Results: On the HandPD test set, the model achieved 97.28% accuracy, 96.60% precision, 96.62% recall, and 96.54% F1-score, outperforming all CNN, transformer, and ML-based baselines compared. Five-fold cross-validation produced a mean accuracy of 96.58%. On the NewHandPD dataset, accuracy, precision, recall, and F1-score were all 95.45%. Discussion: The quantum-classical fusion outperforms both single-modality and fully classical variants. Grad-CAM localizes the spatial image regions driving classification and the perturbation-based sensitivity analysis identifies Root Mean Square (RMS) and age as the most influential structured features. Both together make the model's reasoning traceable at the modality level, which is important for decision-making.
Introduction: Parkinson's disease (PD) is a progressive neurodegenerative disorder caused by the loss of dopaminergic neurons in the substantia nigra, presenting with motor and non-motor symptoms in roughly 2-3% of the global population above age 60. Early detection is difficult because symptoms are subtle and often indistinguishable from normal aging. The MDS-UPDRS rating scale, the current clinical standard, is time-intensive, subjective, and requires experienced clinicians. Most computational approaches are unimodal and do not use image and structured clinical data in combination. Methods: We propose a hybrid quantum-classical dual-track multimodal network that classifies PD patients and healthy controls from hand-drawn spiral and meander patterns. The first track, the Topological Visual-Spatial Feature Encoder Network (TVSFE), uses a ghost module-based CNN with Cross-Dimensional Attention Bottleneck (CDAB) blocks incorporating coordinate attention, squeeze-and-excitation, and triplet attention, followed by a quantum variational circuit with amplitude embedding. The second track, the Variational Quantum Feature Mapping Network (VQFMN), encodes structured clinical and demographic data through RY rotation gates and strongly entangling layers. Outputs from both tracks are concatenated and passed through fully connected layers for classification. Results: On the HandPD test set, the model achieved 97.28% accuracy, 96.60% precision, 96.62% recall, and 96.54% F1-score, outperforming all CNN, transformer, and ML-based baselines compared. Five-fold cross-validation produced a mean accuracy of 96.58%. On the NewHandPD dataset, accuracy, precision, recall, and F1-score were all 95.45%. Discussion: The quantum-classical fusion outperforms both single-modality and fully classical variants. Grad-CAM localizes the spatial image regions driving classification and the perturbation-based sensitivity analysis identifies Root Mean Square (RMS) and age as the most influential structured features. Both together make the model's reasoning traceable at the modality level, which is important for decision-making.
Parkinson’s disease (PD) is a progressive neurodegenerative disorder affecting millions worldwide, where early and accurate diagnosis plays a critical role in improving patient outcomes. While classical machine learning methods have shown strong performance in PD classification using clinical and acoustic data, they remain limited in capturing complex nonlinear feature interactions in high-dimensional spaces. This study explores the application of quantum machine learning (QML) techniques for PD prediction, focusing on Variational Quantum Classifiers (VQC) and Quantum Support Vector Machines (QSVM), and compares their performance with strong classical baselines including Logistic Regression, SVM, Random Forest, Grid Search–optimized models, and XGBoost. Experiments were conducted using stratified train–test splits and validated through cross-validation and statistical testing. Results show that classical models achieve competitive performance, with XGBoost reaching 91.9% accuracy. However, quantum models demonstrate slightly improved performance, with the VQC using EfficientSU2 achieving the highest accuracy of 93.1%, followed by the Real Amplitudes VQC (92.6%) and QSVM (92.2%). These improvements are consistent across evaluation metrics including F1-score and recall. Statistical analysis using 10-fold cross-validation indicates that the VQC (EfficientSU2) shows a significant improvement over classical baselines ( p = 0.031), while other quantum models do not achieve statistical significance over XGBoost ( p > 0.05). McNemar’s test on the independent test set confirms no significant difference in final prediction distributions between quantum and classical models. Overall, the findings suggest that quantum machine learning models are competitive with state-of-the-art classical approaches, offering marginal but consistent performance gains. This study highlights the potential of quantum-enhanced learning frameworks for biomedical classification tasks, while also emphasizing that their advantages remain incremental rather than definitive under current simulation-based implementations.
The prompt detection of neurodegenerative diseases (NDDs) like Alzheimer's Disease (AD), Mild Cognitive Impairment (MCI), and Parkinson's Disease (PD) is essential yet difficult. This research proposes a hybrid quantum-classical machine learning methodology to categorize NDD using multimodal brain imaging data.ADNI and PPMI datasets' MRI, PET, and fMRI images were preprocessing by Python-based tools such as Scikit-image, SimpleITK, and FreeSurfer via Nipype. Major steps were denoising, registration, normalization, segmentation, and extraction of features from structural and functional imaging. Training was done with a Quantum Support Vector Machine (QSVM) using Qiskit's ZZFeatureMap and QuantumKernel and compared with a conventional SVM. Performance measures-precision, recall, F1-score, class accuracy, AUC, and 10-fold cross-validation-were calculated. QSVM performed better than the standard SVM on all metrics, with 91.25% overall accuracy and macro F1-score of 0.913 as opposed to 87.75% and 0.879 for the SVM. The results validate QSVM's advantage in handling complex, high-dimensional neuroimaging data and its relevance to aiding clinical diagnosis. Keywords: Quantum Machine Learning, Neuroimaging, QSVM, Alzheimer’s Disease, Parkinson’s Disease, Multimodal 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.
Diagnosing multimodal medical conditions necessitates seamlessly integrating the insights of both medical images and physiological signals, aiming to boost disease detection accuracy and efficiency in clinical decision making. However, existing deep learning models struggle to exploit sophisticated cross-modal relations and higher-order feature interactions efficiently and computationally. To overcome such shortcomings, we propose a Quantum-Deep Hybrid Architecture to jointly learn the healthcare signal and image features by incorporating Variational Quantum Circuits (VQCs) into a combined architecture based on Convolutional Neural Networks (CNNs) and 1D-CNNs. In our approach, medical image data and physiological signal data pass through distinct CNN and 1D-CNN branches to extract spatial and temporal features, respectively. These encoded, lower-dimensional feature vectors are encoded into quantum states via angle encoding and learn quantum-enhanced features via a Variational Quantum Circuit. The derived quantum and classical features are fused via fully connected layers and a softmax activation function for classification. Moreover, we exploit the Bayesian optimization to select the optimum hyper-parameters and integrate both privacy-preserving feature encoding and secure data processing into the framework. Empirical evaluation results reveal that the developed model achieves overall classification accuracy, precision, recall, F1-score of 94.8%, 95.1%, 94.3%, 94.7%, respectively, with an average AUC of 0.968, surpassing the contemporary CNN and multimodal deep learning baselines. Our proposed hybrid model also shows competitive performance under varying levels of noise and demonstrates excellent stability. The results also suggest the developed framework offers an efficient, robust and scalable approach to next-generation multimodal healthcare analytics and intelligent clinical decision-making systems.
Parkinson Disease is a progressive neurodegenerative disorder for which accessible, low-cost screening methods remain limited. Sustained-vowel voice recordings contain measurable acoustic biomarkers associated with disease-related dysphonia that can be acquired without specialized equipment. This protocol describes a four-qubit quantum-classical hybrid convolutional neural network (QI-HCNN) that combines a parameterized quantum circuit with a shallow classical classification layer to classify Parkinson Disease using dimensionally reduced acoustic voice features. The protocol also benchmarks the proposed approach against a dimensionality-matched classical neural network and gradient-boosted tree classifiers trained on both the reduced and full feature sets. Using a publicly available, de-identified dataset comprising 195 voice recordings from 31 individuals, the QI-HCNN achieved an area under the receiver operating characteristic curve of 0.78 using three-fold stratified cross-validation, compared with 0.87 for the matched classical neural network and 0.94-0.95 for the gradient-boosted baselines trained on the reduced and full feature sets, respectively. A controlled ablation analysis demonstrated that one of the two entanglement operations in the circuit could not influence the measured output by design, whereas the remaining entanglement operation reduced, rather than improved, classification performance relative to a non-entangled variant. Patient-grouped cross-validation further showed that performance estimates varied substantially depending on the individuals assigned to the test set, reflecting the limited cohort size. This protocol therefore provides a fully specified and independently reproducible quantum-classical baseline together with a data-supported evaluation of the strengths and limitations of the current circuit design, providing a methodological foundation for future investigations rather than supporting claims of diagnostic readiness.
Abstract Neuro-imaging data is necessary for timely and correct diagnosis of the disease Alzheimer’s (AD). Early clinical intervention is challenging due to the high-dimensional nature of the images, making it computationally difficult to work on representations, sparse annotated datasets and inter-subject variance. Deep convolutional VGG, ResNet, DenseNet, MobileNet and other neural networks (CNNs) have shown good performance. Diagnostic performance, but they are based on a high number of parameters and require highly trained personnel. Data can often times restrict resilience to data-skimy clinical settings. To address these challenges, the current paper presents a hybrid Quantum Machine Learning (QML) architecture for Alzheimer’s disease Detection and classification based on a combination of classical CNN-based feature extraction and Variational quantum classifier. In the suggested method, diseased, relevant, and compact features are extracted using pre-trained CNN backbones. Data from structural MRI scans is later translated into quantum states and computed. With parameterised variational quantum circuits optimised with a hybrid quantum-classical learning loop. The proposed method has been experimentally evaluated on benchmark datasets of MRI scans of Alzheimer’s patients. The hybrid CNNQML model has a mean classification accuracy of about 93% which is similar to deep CNN models, including ResNet and DenseNet (94%), and much more. Beating lightweight models like MobileNet and shallow CNNs by six percentage points. It is worth noting that the hybrid QML model performs better in conditions with limited training data. Performance degradation is observed in accuracy and AUC for all CNN variants, with up to 5% lower performance to deep CNNs. Cross-validation, confidence interval, paired significance test, and statistical validation. Effect size analysis proves the fact that the hybrid model produces statistically significant performance, as well as cases of clinically meaningful improvements over lightweight CNN baselines. Similar to the state-of-the-art deep networks with significantly reduced trainable parameters in the classification stage. These findings show that hybrid QML is a data-efficient and powerful substitute in the diagnosis of Alzheimer’s. On the whole, this is a work that sets a practical way to integrate quantum machine learning into clinical neuro-imaging pipelines and hybrid settings. QML has supplementary deep-learning enhancement to classics in next-generation Alzheimer’s Decision-support systems.