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
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
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.
Alzheimer's Disease (AD) first affects the brain parts that are associated with learning. It is one of the most prevalent forms of Dementia. Its early diagnosis is crucial for properly managing the disease treatment because it is chronic and irreversible. This paper introduces a holistic approach to achieve early detection of AD using the hippocampus and transfer learning. Here we have compared the results from the classical machine learning model – Support Vector Machine (SVM) and the Quantum Support Vector Machine (QSVM) model. QSVM - a quantum variant of standard SVM algorithm. It is developed using techniques such as quantum kernel estimation in quantum computing for more efficient processing of high-dimensional data than standard SVMs.. The most common symptoms of Alzheimer’s are loss of memory and cognitive impairment. This stems from the destruction and death of nerve cells in the brain related to memory. Mild Cognitive Impairment (MCI) is a condition between normal brain function and Alzheimer’s. From the prodromal MCI stage, it progresses gradually to dementia. Several studies show that Alzheimer’s develops at a rate of 10–15% per year from MCI. The early identification of patients with MCI may halt/delay the progression from MCI stage to Alzheimer’s. The results with the precision of 0.85 with the QSVM compared to the precision of 0.78 and recall of 1.00 compared to the 0.89 with the traditional SVM shows that our technique of Quantum Machine Learning (QML) is very useful in Alzheimer’s early diagnosis.
As a degenerative disease that seriously affects the central nervous system, early diagnosis of Parkinson's disease is important for slowing down the course of the disease and improving patients' quality of life.In recent years, machine learning has shown great potential in medical data analysis, and support vector machines (SVMs) are widely used in classification and prediction tasks.However, traditional SVMs have some limitations in high dimensionality and complex data processing.With the rapid development of quantum computing, quantum support vector machines (QSVMs) have become an emerging technology that has attracted much attention due to its ability to process largescale data and its potential for modeling high-dimensional features.In this paper, we compare traditional SVMs and QSVMs in the analysis of Parkinson's disease data, and evaluate their performance in terms of classification accuracy.The experiments are conducted on a publicly available dataset and analyzed in detail through the steps of model construction, data preprocessing, algorithm implementation, and performance evaluation.The results show that QSVM outperforms traditional SVM in complex feature space, which demonstrates its potential advantages and application prospects in medical data analysis.
Alzheimer's disease affects our thinking and behavior. It is one of the types of dementia. It initially affects the brain’s learning regions. Since the disease is both progressive and irreversible, early diagnosis is vital for effective treatment management. This research presents a comprehensive, end-to-end approach for early detection of Alzheimer’s by focusing on the hippocampus and leveraging transfer learning. The study compares the performance of a Quantum Convolutional Neural Network (QCNN) with that of a Quantum Support Vector Machine (QSVM). Findings suggest that QCNN is more efficient at handling high-dimensional data compared to QSVM. Alzheimer's disease is mainly defined by the deterioration of memory and cognitive abilities, which results from the degeneration and death of neurons that play a crucial role in memory function. Mild Cognitive Impairment (MCI), which sits between normal cognitive function and Alzheimer’s, offers a critical window for early intervention. Diagnosing MCI early may help delay or even prevent the onset of Alzheimer’s. The results of our experiment show that the QCNN model achieved a precision of 0.88, recall of 0.96, and an accuracy of 0.59. In contrast, the QSVM model recorded a precision of 0.85, recall of 1.00, and an accuracy of 0.85. These findings indicate that both quantum machine learning models demonstrate strong potential and complement each other in performance.
Neurological disorders often cause progressive cognitive and motor impairments, with autosomal recessive diseases presenting diagnostic challenges due to overlapping symptoms and gradual onset. Wilson's Disease (WD), caused by ATP7B gene mutations, leads to toxic copper accumulation in the liver and brain, resulting in hepatic, neurological, and psychiatric dysfunction. Conventional diagnostic methods, such as serum ceruloplasmin tests, copper assays, and liver biopsies, are invasive and inconsistent in early detection. To address these limitations, this work proposes a hybrid Spiking Neural Network–Quantum Support Vector Machine (SNN-QSVM) framework for WD classification. The model uses neuromorphic learning and quantum-enhanced classification on demographic, clinical, symptomatic, genetic, and lifestyle features from a 60,000-sample dataset. Evaluated using accuracy, precision, recall, F1-score, and AUC-ROC, the framework achieved 94% accuracy, demonstrating strong diagnostic capability. Future work includes multimodal clinical integration and scalable quantum deployment.
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.