This study proposes a hybrid architecture of Quantum Neural Networks (HQNN) combining quantum computing with classical machine learning to improve the early diagnosis of Multiple Sclerosis (MS). The model integrates simulated multimodal data, including Ultra-High Field MRI (7T), liquid biomarkers (Neurofilament Light Chain - NfL), and genetic data. By utilizing variational algorithms such as VQE and QAOA, the simulated HQNN demonstrates superior performance in capturing complex patterns in high-dimensional spaces, achieving higher accuracy and AUC compared to traditional models like SVM and Random Forest. The research also emphasizes Explainable AI (XAI) to ensure clinical transparency.
This study proposes a hybrid architecture of Quantum Neural Networks (HQNN) combining quantum computing with classical machine learning to improve the early diagnosis of Multiple Sclerosis (MS). The model integrates simulated multimodal data, including Ultra-High Field MRI (7T), liquid biomarkers (Neurofilament Light Chain - NfL), and genetic data. By utilizing variational algorithms such as VQE and QAOA, the simulated HQNN demonstrates superior performance in capturing complex patterns in high-dimensional spaces, achieving higher accuracy and AUC compared to traditional models like SVM and Random Forest. The research also emphasizes Explainable AI (XAI) to ensure clinical transparency.
Abstract Accurate detection of multiple sclerosis (MS) lesions in magnetic resonance imaging (MRI) requires robust deep learning models to capture subtle spatial and textural features. We introduce hybrid quantum-classical transfer learning algorithms for MS classification using axial and sagittal MRI scans, combining classical convolutional neural networks (CNNs) including EfficientNetB3, ResNet50, DenseNet121 with parameterized quantum circuits to enhance feature representation via entanglement and quantum-specific non-linearities. Quantum layers are trained end-to-end with classical backbones via backpropagation, enabling seamless integration of quantum-enhanced features. For axial MRI, QResNet50 achieved a high accuracy of 97.58% and AUC of 99.31%, while QDenseNet121 reached 97.28% accuracy and 99.13% AUC. For sagittal MRI, classical ResNet50 excelled with 99.15% accuracy and 99.93% AUC, while QEfficientNetB3 improved accuracy (97.46% to 98.30%) but reduced AUC (99.51% to 99.32%), and QDenseNet121 achieved 98.87% accuracy and 99.83% AUC. Hybrid models showed mixed results, with QCNN underperforming, suggesting quantum benefits are architecture-dependent. Despite simulated quantum circuits mitigating hardware limitations, our results demonstrate the potential to enhance diagnostic performance in specific architectures. This work clarifies a foundational step toward quantum-enhanced deep learning for clinical applications, opening research directions in quantum-aware transfer learning and error mitigation for biomedical imaging.
Multiple sclerosis (MS) is a condition that affects the brain or central nervous system, which can result in issues with the optic nerve, brain, and spinal cord. Multiple sclerosis is thought to affect 2.8 million people. This research focuses on the diagnosis of multiple sclerosis using Quantum machine learning (QML) classifiers. Several models have been created utilizing various data sets, such as clinical and magnetic resonance imaging (MRI) data. The quantum machine learning has emerged by the merging of classical machine learning and quantum information processing as a result of the superiority and advancement of quantum computing in various fields (such as encryption, machine learning, and healthcare). The experimental results present a comparative analysis of classical machine learning and quantum machine learning algorithms for the problem under consideration.
Early and correct diagnosis plays a key role in enhancing the outcome in brain tumor and lung cancer patients. Traditional Artificial Intelligence (AI) and Deep Learning (DL) models are handicapped by expensive computational costs and challenges in handling high-dimensional medical data. This research article presents a Quantum AI based Diagnostics System employing a hybrid quantum-classical computing model. The framework combines traditional feature preprocessing with the exponential power of quantum computing. For classification in brain tumors (e.g., LGGs/HGGs), the quantum core uses a Hybrid Quantum-Classical Integrated Neural Network (HQCINN) or Variational Quantum Classifier (VQC). For lung cancer prediction, the framework might employ quantum-enhanced clustering such as Quantum-Enhanced K-Medoids or optimization models such as Quantum --Genetic Binary Grey Wolf Optimizer (Q-GBGWO) with Extreme Learning Machines (ELM). This hybrid methodology is intended to achieve superior diagnostic efficacy and speed across both MRI and CT modalities, laying the groundwork for faster and more individualized clinical diagnostics.
Chronic diseases—such as diabetes, cardiovascular disorders, and chronic respiratory conditions—account for over 70% of global deaths annually, with late diagnosis being a primary contributor to poor outcomes. While machine learning (ML) models have shown promise in early detection, they often suffer from limited generalizability, data heterogeneity, and insufficient interpretability in clinical settings. This paper introduces a novel hybrid quantum-classical machine learning (HQML) framework that synergistically combines the pattern recognition power of classical deep neural networks with the high-dimensional optimization capabilities of quantum algorithms to enhance diagnostic accuracy, robustness, and early signal detection in chronic disease prediction. Using multimodal electronic health record (EHR) data—including clinical, genomic, and lifestyle variables—we train a quantum-enhanced feature selector followed by a classical interpretable classifier (e.g., SHAP-augmented XGBoost). The quantum component leverages a variational quantum circuit to identify non-linear, high-order feature interactions that classical methods often miss. Validated on datasets from the National Health and Nutrition Examination Survey (NHANES) and UK Biobank, our model achieves 94.7% AUC for type 2 diabetes prediction five years before clinical diagnosis—outperforming state-of-the-art baselines by 6.2%. Crucially, the framework maintains interpretability through post-hoc explainability and reduces data bias via fairness-aware quantum embedding. This research bridges quantum computing and public health, offering a scalable, ethically grounded diagnostic paradigm. By enabling earlier, more accurate, and equitable predictions, the HQML framework has significant potential to transform preventive care and reduce the global burden of chronic disease.
Alzheimer's disease (AD) is a debilitating neurodegenerative condition that affects millions globally, with early diagnosis being crucial for effective intervention and treatment. This project explores Quantum Neural Networks (QNN) combined with Magnetic Resonance Imaging (MRI) data to enhance diagnosis accuracy. This hybrid quantum- classical approach leverages advanced preprocessing techniques, uncovering intricate patterns in MRI data. The study highlights the potential of quantum computing in revolutionizing early-stage Alzheimer's detection, achieving superior performance over traditional methods. Keywords— Alzheimer’s disease, Quantum Computing, MRI, Machine Learning, Quantum Neural Networks
Purpose This study investigates the integration of quantum-enhanced machine learning (QML) into medical image analysis, focusing on classification and feature extraction tasks. It aims to evaluate whether hybrid quantum-classical models can overcome the limitations of traditional machine learning in handling high-dimensional, clinically relevant imaging data. Design/methodology/approach Hybrid architectures combining classical preprocessing with quantum components, specifically variational quantum circuits (VQCs) and quantum kernel methods, were developed and implemented in simulated quantum environments using Qiskit and PennyLane on Google Colab. Publicly available datasets (ChestX-ray14, Brain Tumor MRI and CT Hemorrhage) were preprocessed and encoded into quantum states. Model performance was evaluated against classical baselines (MLP and CNN) using accuracy, precision, recall, F1-score, convergence speed and trainable parameters. Findings Quantum-enhanced models achieved competitive or superior performance compared to classical models with significantly fewer trainable parameters and faster convergence. The Quantum Kernel SVM demonstrated the highest precision (89.1%) and F1-score (88.5%) using only 8 parameters, while VQC models converged up to 2× faster than classical baselines. These results highlight the potential of QML to improve efficiency, robustness and scalability in diagnostic imaging workflows, particularly in resource-constrained or data-limited settings. Research limitations/implications The study relies on simulated quantum environments, which do not capture hardware noise or qubit limitations. Future work will extend to real quantum devices, multimodal data integration and interpretability strategies. Practical implications Quantum kernel methods and lightweight VQC architectures enable faster, resource-efficient diagnostic modeling, making them suitable for clinical environments with limited computational capacity or time-sensitive decision-making needs. Social implications Improving the efficiency and accessibility of medical image analysis has the potential to support faster diagnosis and enhance healthcare delivery, particularly in underserved or resource-limited regions. Originality/value This study provides a practical evaluation of hybrid quantum-classical models applied to multiple real medical imaging datasets in a reproducible simulated environment. It offers an early-stage framework demonstrating how QML techniques can be integrated into existing machine learning pipelines for medical image analysis.