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.
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.
Accurate and computationally efficient classification of brain tumors from MRI scans is critical for deployment in resource-constrained clinical settings, where conventional deep CNN architectures-despite achieving strong accuracy-often require large parameter counts that limit scalability. This work proposes a hybrid quantum-classical deep learning framework that combines a classical convolutional feature extractor with a variational quantum circuit (VQC) for multi-class tumor classification. Spatial features extracted by the CNN backbone will be dimensionality-reduced and encoded into quantum states using angle encoding; a parameterized quantum circuit will then process these features in a higher-dimensional Hilbert space, leveraging superposition and entanglement to capture non-linear feature correlations. Measurement outputs from the quantum layer will be passed to a classical fully connected layer for classification into glioma, meningioma, pituitary tumor, and no-tumor categories. The proposed framework will be evaluated on a publicly available benchmark MRI dataset and benchmarked against state-of-the-art classical CNN architectures in terms of accuracy, precision, recall, F1-score, and parameter efficiency. This study aims to demonstrate that quantum-enhanced layers can achieve competitive classification performance while substantially reducing trainable parameters, offering a practical direction for efficient, quantum-assisted medical image analysis.
The rapid expansion of clinical data in modern healthcare requires analytical systems capable of uncovering intricate patterns and supporting accurate diagnostic decisions. Quantum machine learning (QML) offers significant potential for modeling higher-order feature interactions and accelerating computation beyond classical approaches. This paper introduces an improved hybrid architecture that fuses an inception-based attentional VGG (IAV) network with a quantum variational classifier (QVC) constructed using parameterized quantum circuits (PQCs). The framework begins with min-max normalization to stabilize heterogeneous clinical attributes and enhance training convergence. Deep discriminative features are then extracted through the IAV model, followed by quantum-driven classification using variational layers optimized by classical routines. The MIMIC-III clinical dataset is employed to validate the proposed system on large-scale healthcare records. Performance is measured using accuracy, precision, recall, and F1-score. The enhanced hybrid model achieves 97.28% accuracy, 97.16% precision, 96.65% recall, and a 97.38% F1-score, surpassing established methods including support vector machine (SVM) (89.23%), quantum support vector machine (QSVM) (90.13%), and QVKSVM (97.34%). The findings confirm that integrating deep learning with quantum variational optimization strengthens scalability, reduces computational overhead, and establishes a powerful foundation for next-generation healthcare analytics.
Quantum machine learning has emerged as a promising approach to improve feature extraction and classification tasks in high-dimensional data domains such as medical imaging. In this work, we present a hybrid Quantum-Classical Convolutional Neural Network (QCNN) architecture designed for the binary classification of the BreastMNIST dataset, a standardized benchmark for distinguishing between benign and malignant breast tumors. Our architecture integrates classical convolutional feature extraction with two distinct quantum circuits: an amplitude-encoding variational quantum circuit (VQC) and an angle-encoding VQC circuit with circular entanglement, both implemented on four qubits. These circuits generate quantum feature embeddings that are fused with classical features to form a joint feature space, which is subsequently processed by a fully connected classifier. To ensure fairness, the hybrid QCNN is parameter-matched against a baseline classical CNN, allowing us to isolate the contribution of quantum layers. Both models are trained under identical conditions using the Adam optimizer and binary cross-entropy loss. Experimental evaluation in five independent runs demonstrates that the hybrid QCNN achieves statistically significant improvements in classification accuracy compared to the classical CNN, as validated by a one-sided Wilcoxon signed rank test (p = 0.03125) and supported by large effect size of Cohen's d = 2.14. Our results indicate that hybrid QCNN architectures can leverage entanglement and quantum feature fusion to enhance medical image classification tasks. This work establishes a statistical validation framework for assessing hybrid quantum models in biomedical applications and highlights pathways for scaling to larger datasets and deployment on near-term quantum hardware.
Introduction: Medical image classification is a crucial task in cancer diagnosis, relying on the accurate analysis of high-dimensional imaging data. While Convolutional Neural Networks (CNNs) have shown great success in this domain, their performance is often limited by the shallow feature expressiveness and overfitting, particularly in small or heterogeneous datasets. Methods: Quantum machine learning offers new opportunities through high-dimensional representations and nonlinear transformations. In this work, we propose a Quantum-Enhanced Sandwich Convolutional Neural Network (QSCNN), a layered hybrid architecture that integrates quantum and classical modules. The model employs a quanvolutional layer for localized quantum feature extraction, followed by conventional convolution and pooling for hierarchical representation learning, and a variational quantum classifier for nonlinear decision-making. Results: QSCNN achieved higher accuracy and training stability than classical CNNs and QCCNN baselines across three medical imaging tasks.: brain tumor MRI, skin cancer dermoscopy, and lung cancer CT. Circuit depth analysis revealed a trade-off between expressiveness and robustness, and additional experiments with depolarizing noise confirmed the model’s resilience under realistic quantum error conditions. Discussion: This suggests that circuit design choices influence hybrid model behavior and generalization, supporting the feasibility of quantum-enhanced methods for small-sample medical imaging. However, the current evaluation is limited to relatively small benchmark datasets, and broader validation on large-scale data will be essential to confirm clinical applicability. Conclusion: In summary, QSCNN presents a feasible hybrid framework for enhancing medical image classification with quantum features. While preliminary, our results suggest potential advantages in accuracy and stability under NISQ conditions.
Medical image classification is a critical component of modern healthcare; however, accurate diagnosis remains challenging due to limited annotated datasets, class imbalance, and the high dimensionality of medical imaging data. To address these challenges, a hybrid quantum-classical neural network (HQCNN) is proposed, integrating classical deep learning with variational quantum learning for medical image classification. The proposed architecture combines a five-layer convolutional neural network (CNN) for hierarchical feature extraction with a lightweight 4-qubit variational quantum circuit (VQC) incorporating quantum state encoding, superposition and entanglement mechanisms, and a quantum attention-Fourier (QAF) module. This hybrid design aims to improve nonlinear feature representation and quantum parameter efficiency while maintaining a shallow quantum circuit suitable for noisy intermediate-scale quantum (NISQ)-era constraints. Experimental evaluation on six MedMNIST benchmark datasets demonstrated competitive performance across both binary and multi-class classification tasks. HQCNN achieved 98.88% accuracy on the binary subset of PathMNIST (classes 0 vs. 1), 97.61% accuracy on the multi-class OrganAMNIST dataset, and 86.29% accuracy on BreastMNIST. Comparative experiments and statistical analyses demonstrated consistent improvements over the controlled BHQNN baseline, while component-wise ablation studies showed that the QAF module, superposition and entanglement mechanisms, and expressive parameterized rotations contributed to classification performance in a complementary and dataset-dependent manner. Moreover, HQCNN reduced the number of trainable quantum parameters by approximately 55.6% compared with the baseline hybrid quantum neural network (BHQNN). Noise-aware simulations further showed that the model retained relatively stable predictive performance under moderate depolarizing noise, supporting further evaluation under near-term quantum computing conditions. Overall, the results demonstrate that HQCNN provides a parameter-efficient hybrid quantum-classical framework for medical image classification and offers a promising foundation for further investigation of quantum-enhanced medical image analysis.
Medical image classification is an important component in computer-aided diagnosis and currently popular deep learning models are found to be inefficient in capturing the complex spatial relationships between the medical images while performing efficiently and generalizing well for multi-class classification.In this work, we introduce a novel Adaptive Variational Quantum Convolutional Neural Network (AVQCNN) based on quantum feature encoding and adaptive variational quantum convolution, multi-scale entanglement fusion, hybrid quantum-classical optimization, and uncertainty-aware prediction to boost the diagnostic accuracy.It has been successfully applied to PennyLane and TensorFlow, and evaluated using the MedMNIST v2 multi-class medical imaging benchmark with standard metrics.Implements and tests this framework with PennyLane and TensorFlow and uses MedMNIST v2 benchmark for medical imaging with standard performance metrics.Through experiments, an accuracy of 98.21%,precision of 97.94%,recall of 97.73%,F1 score of 97.83% and an AUC of 0.996 were achieved with training time of only 82 minutes, inference time of 9.4ms/image, 6.3 million parameters and 5.4GB of GPU memory.The results demonstrate its potential to provide a scalable and reliable quantum-enhanced framework for next-generation intelligent clinical decision support system, with its ability to boost both the classification accuracy and computation efficiency.