Alzheimer’s disease prediction requires robust mod eling of heterogeneous medical data, including structural MRI representations and structured clinical attributes. This study presents a controlled multi-modality benchmarking framework comparing optimized classical ensemble learning methods with a Variational Quantum Classifier (VQC) under identical prepro cessing and validation protocols. MRI features are reduced using Principal Component Analysis (PCA), while structured clinical attributes are modeled using Random Forest, XGBoost, Voting, and Stacking ensembles. A hybrid quantum–classical pipeline is implemented using Qiskit and PennyLane to evaluate near-term quantum feasibility under NISQ constraints. Experimental results demonstrate that stacking ensemble mod els achieve 95.3% accuracy on clinical data and 91.5% on MRI data, significantly outperforming the VQC, which achieves 71.4% accuracy under the same evaluation conditions. Statistical testing confirms that this performance gap is significant. These findings indicate that optimized classical ensemble learn ing remains superior for current medical prediction tasks, while variational quantum classification remains exploratory under present hardware limitations. Keywords: Medical Image Analysis; Alzheimer’s Disease; Multi-Modal Learning; Ensemble Learning; Variational Quantum Classification; Hybrid Quantum-Classical Learning; Clinical Data Analytics; Noisy Intermediate-Scale Quantum (NISQ) Com puting.
Alzheimer’s disease prediction requires robust mod eling of heterogeneous medical data, including structural MRI representations and structured clinical attributes. This study presents a controlled multi-modality benchmarking framework comparing optimized classical ensemble learning methods with a Variational Quantum Classifier (VQC) under identical prepro cessing and validation protocols. MRI features are reduced using Principal Component Analysis (PCA), while structured clinical attributes are modeled using Random Forest, XGBoost, Voting, and Stacking ensembles. A hybrid quantum–classical pipeline is implemented using Qiskit and PennyLane to evaluate near-term quantum feasibility under NISQ constraints. Experimental results demonstrate that stacking ensemble mod els achieve 95.3% accuracy on clinical data and 91.5% on MRI data, significantly outperforming the VQC, which achieves 71.4% accuracy under the same evaluation conditions. Statistical testing confirms that this performance gap is significant. These findings indicate that optimized classical ensemble learn ing remains superior for current medical prediction tasks, while variational quantum classification remains exploratory under present hardware limitations. Keywords: Medical Image Analysis; Alzheimer’s Disease; Multi-Modal Learning; Ensemble Learning; Variational Quantum Classification; Hybrid Quantum-Classical Learning; Clinical Data Analytics; Noisy Intermediate-Scale Quantum (NISQ) Com puting.
Alzheimer's disease prediction requires robust mod eling of heterogeneous medical data, including structural MRI representations and structured clinical attributes. This study presents a controlled multi-modality benchmarking framework comparing optimized classical ensemble learning methods with a Variational Quantum Classifier (VQC) under identical prepro cessing and validation protocols. MRI features are reduced using Principal Component Analysis (PCA), while structured clinical attributes are modeled using Random Forest, XGBoost, Voting, and Stacking ensembles. A hybrid quantum–classical pipeline is implemented using Qiskit and PennyLane to evaluate near-term quantum feasibility under NISQ constraints. Experimental results demonstrate that stacking ensemble mod els achieve 95.3% accuracy on clinical data and 91.5% on MRI data, significantly outperforming the VQC, which achieves 71.4% accuracy under the same evaluation conditions. Statistical testing confirms that this performance gap is significant. These findings indicate that optimized classical ensemble learn ing remains superior for current medical prediction tasks, while variational quantum classification remains exploratory under present hardware limitations.
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.
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) presents significant diagnostic challenges owing to the subtle morphological similarities observed in the early stages, with traditional deep learning approaches often struggling to distinguish between the various stages of disease progression via structural Magnetic Resonance Imaging (MRI) data. Quantum computing offers unique advantages for medical image analysis, leveraging superposition and entanglement capabilities to process high-dimensional feature spaces beyond the limits of classical computation. This study introduces a hybrid quantum-classical neural network architecture (HQC-Net) for accurate four-class Alzheimer's disease classification, which uses quantum processing to detect patterns that are often invisible to classical spatial analysis methods. The proposed framework integrates classical feature extractors, including a custom CNN and modified ResNet18, with six-qubit variational quantum circuits that employ multiaxis rotation encoding (RY→RZ→RX), a quantum Fourier transform for spectral decomposition, and multihead attention for effective quantum-classical feature fusion. Comprehensive evaluations were conducted on the Kaggle dataset (5,121 samples) and the OASIS dataset (20,000 samples), incorporating realistic quantum noise modelling, including depolarising, amplitude damping, and phase damping channels. The modified QResNet18 configuration achieved a test accuracy of 99.67%, with perfect discriminative capability (AUC = 1.0000) on the OASIS dataset. Quantum processing demonstrated superior detection of very mild dementia (99.86% accuracy), which is crucial for early intervention. The proposed approach outperformed existing quantum-enhanced methods by 3.57 percentage points while effectively handling the increased diagnostic complexity associated with four-class classification. This study demonstrates a practical quantum advantage for multiclass neuroimaging classification, achieving superior diagnostic accuracy while maintaining computational efficiency and clinical deployment feasibility under current Noisy Intermediate-Scale Quantum (NISQ) hardware constraints.
Over the past decade, exceptional progress has been made in deep learning and its applications to medical imaging including image analysis, disease classification, medical image segmentation, and a variety of other tasks. Alzheimer’s disease is a progressive cognitive disorder and the most common cause of dementia in the world. In this research, we present a lightweight hybrid classical-quantum framework leveraging quantum machine learning (QML) using the pennylane library. Consisting of classical convolutional neural network (CNN) and quantum circuits as quantum nodes wrapped in a torch layer of pytorch, the framework has a total of eight layers including the quantum layer, for the classification of Alzheimer’s disease stages from brain MRI images. Using this framework, the 4 -class Alzheimer’s MRI dataset is assessed. This dataset contains 6400 MRI images across four classes of disease stages, available in the Kaggle. The dataset has gone through the Pre-processing stage of augmentation to scale and balance the classes. The proposed hybrid framework achieves an accuracy of 96.26 % in classifying 4 stages of Alzheimer’s Disease, outperforming the classical deep learning models while utilizing substantially fewer parameters. The superior performance of the hybrid framework is compared with the performance of four other classical models: 1. VGG 2. Densenet 3. Enet and 4. Resnet. Grad-CAM (Explainable AI) is employed to assess the proposed framework’s focus on relevant brain regions and to validate its accuracy through consultation with a medical professional. By ensuring alignment between the framework’s attention and clinical expertise, this research enhances the potential for assisting medical professionals in accurately interpreting neurodegenerative disease states and thereby advancing the development of clinically applicable diagnostic tools. It also demonstrates the feasibility of adopting quantum machine learning in medical image analysis and medical AI.
Prostate cancer is among the most prevalent malignancies in men, and early, accurate diagnosis is critical for effective treatment. Traditional machine learning techniques have demonstrated success in analyzing multiparametric magnetic resonance imaging (MRI) and clinical biomarkers; however, their scalability and capacity to model complex feature interactions remain limited. This study proposes an Ensemble Quantum Machine Learning (QML) framework to enhance prostate cancer detection using the PROSTATEx Challenge dataset. Radiomic features were extracted from MRI modalities and clinical attributes, then standardized and reduced using principal component analysis (PCA) to match current quantum hardware constraints. Three quantum classifiers—Quantum Support Vector Machine (QSVM), Variational Quantum Classifier (VQC), and Quantum Neural Network (QNN) - were independently trained and integrated using both soft voting and stacked ensemble strategies. Results from stratified 5-fold cross-validation show that the stacked ensemble outperformed individual models and baseline classifiers, achieving an average accuracy of 88.4%, recall of 89.1%, precision of 87.9%, F1-score of 88.5%, and area under the curve - receiver operating characteristic (AUC-ROC) of 0.94 (stack ensemble). These findings highlight the potential of hybrid quantum-classical ensemble learning to improve diagnostic robustness, particularly in reducing false negatives. Furthermore, validation on real quantum hardware demonstrated consistent performance, underscoring the feasibility of QML in near-term medical applications. This work contributes to the growing intersection of quantum computing and clinical AI, offering a scalable and interpretable approach to precision oncology.