Abstract Pulmonary nodule classification remains constrained by variability in computed tomography (CT) acquisition protocols, annotation quality, and the extreme class imbalance that hinders accurate decision–making in diagnosis. Traditional deep learning approaches have been severely limited by the lack of diversity in training datasets, their tendency to be overly sensitive to noise, and their inability to account for the large number of nonlinear feature interactions present in volumetric structures. Therefore, a dual–phase framework is presented to investigate the application of classical and hybrid quantum–classical (HQC) architectures. Phase 1 evaluates CNN and hybrid Quantum–classical (HQC) feature extractors to quantify quantum expressivity. The HQC model employs a series of layered variational quantum circuits (VQCs) with calibrated rotation gates and structured entanglement patterns to expand the hypothesis space while improving operational stability. HQC achieved superior ROC–AUC (0.89 ± 0.02) and recall (0.86 ± 0.03). Phase 2 benchmarks the performance of Swin–Transformer embeddings with classical classifiers, as well as the HQC for a fair comparison. The HQC constantly outperforms other methods (ROC–AUC = 0.73 ± 0.02), indicating robustly enhanced quantum representations. Conditional Generative Adversarial Networks (CGANs) were used to address the extreme dataset imbalance. This dissertation provides statistically valid evidence that HQC architectures significantly increase both classification sensitivity and stability, thereby offering an opportunity for future research on precision oncology under high-variance, data-constrained conditions.
Abstract Pulmonary nodule classification remains constrained by variability in computed tomography (CT) acquisition protocols, annotation quality, and the extreme class imbalance that hinders accurate decision–making in diagnosis. Traditional deep learning approaches have been severely limited by the lack of diversity in training datasets, their tendency to be overly sensitive to noise, and their inability to account for the large number of nonlinear feature interactions present in volumetric structures. Therefore, a dual–phase framework is presented to investigate the application of classical and hybrid quantum–classical (HQC) architectures. Phase 1 evaluates CNN and hybrid Quantum–classical (HQC) feature extractors to quantify quantum expressivity. The HQC model employs a series of layered variational quantum circuits (VQCs) with calibrated rotation gates and structured entanglement patterns to expand the hypothesis space while improving operational stability. HQC achieved superior ROC–AUC (0.89 ± 0.02) and recall (0.86 ± 0.03). Phase 2 benchmarks the performance of Swin–Transformer embeddings with classical classifiers, as well as the HQC for a fair comparison. The HQC constantly outperforms other methods (ROC–AUC = 0.73 ± 0.02), indicating robustly enhanced quantum representations. Conditional Generative Adversarial Networks (CGANs) were used to address the extreme dataset imbalance. This dissertation provides statistically valid evidence that HQC architectures significantly increase both classification sensitivity and stability, thereby offering an opportunity for future research on precision oncology under high-variance, data-constrained 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.
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.
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.
Deep learning techniques have significantly enhanced medical image classification for disease diagnosis. However, traditional deep learning models face several challenges, such as dependence on large annotated datasets, complex architectural layers, and millions of trainable parameters, which can increase computational load and cause overfitting. Although quantum computing offers potential for parallel processing and improved feature representation, its practical use is limited by the current capabilities of Noisy Intermediate-Scale Quantum (NISQ) hardware. To overcome these issues, Hybrid Quantum–Classical Convolutional Neural Networks (HQCNNs) have been developed, using quantum properties like superposition and entanglement to improve feature extraction and classification of medical images. This review offers a thorough analysis of 50 peer-reviewed studies published from 2021 to 2026 and introduces a taxonomy of HQCNN architectures. It classifies existing approaches into three categories: Quantum-to-Classical, Classical-to-Quantum, and Hybrid Quantum Transfer Learning. The studies reviewed encompass a diverse range of medical imaging applications, including binary and multi-class disease classification using X-ray, CT, MRI, ultrasound, dermoscopic, retinal, and mammographic images. Additionally, the review compares HQCNNs with their classical CNN counterparts, exploring how factors like quantum circuit design, dataset size, class imbalance, task complexity, evaluation protocols, and hardware limitations influence classification performance. It concludes with an analysis of the key limitations of current HQCNN models, discusses challenges in deploying NISQ devices practically, and proposes future research directions to bridge the gap between experimental HQCNN frameworks and reliable clinical diagnostic systems.
This paper surveys the emerging field of hybrid quantum–classical (HQC) architectures for medical image analysis, with a particular focus on COVID-19 classification as a rep-resentative application. Despite extensive progress in quantum machine learning, there is still a lack of structured taxonomy-driven analysis that systematically characterizes how quantum components are integrated within hybrid imaging pipelines. This study pro-vides a structured review of representative HQC models, categorizing existing approaches based on the functional role of quantum modules, including front-end processing, feature extraction, and classification layers. A comparative analysis is conducted to examine how these architectural design choices relate to reported performance across different ex-perimental settings. The review shows that hybrid models can achieve strong binary classification performance under simulation conditions, particularly when quantum cir-cuits are used as feature extractors. However, performance in multi-class scenarios remains inconsistent and highly sensitive to encoding strategies, circuit design, and optimization settings. In addition, most existing studies rely on simulator-based evaluations, limited or single-source datasets, and non-standardized benchmarking protocols, which restrict reproducibility and generalization. Real-hardware validation remains scarce, and no consistent evidence of task-level quantum advantage over optimized classical baselines has been demonstrated. Overall, current HQC-based approaches should be considered exploratory rather than clinically validated solutions. Future progress requires stand-ardized benchmarking, multi-institutional validation, hardware-aware evaluation, and the development of interpretable hybrid architectures that respect NISQ-era constraints.
Accurate classification of glioma grades from magnetic resonance imaging (MRI) is essential for clinical decision-making in neuro-oncology. Although deep learning performance has been impressive with classical models, they struggle with high-dimensional medical imaging data and generalise poorly beyond their training data, especially in time- and resource-constrained settings. In light of the aforementioned challenges, we propose QuantumMedDx, a hybrid quantum–classical learning framework for classifying gliomas using MRI. The framework combines quantum feature encoding and variational quantum circuits with classical neural inference to improve diagnostic performance. The base model, QImageNet, uses amplitude-based quantum encoding for writing, entanglement-enabled parameterised quantum circuits (EPQCs) as feature extractors, and classical dense layers for classifying HGG and LGG from multimodal MRI slices. We demonstrate the effectiveness of the proposed approach on the BraTS 2021 benchmark dataset using a patient-aware 5-fold cross-validation protocol. Experimental results show that QuantumMedDx achieves accuracies of 94.12%, 93.30%, and 96.42%; F1-scores of 93.30% and 96.42%; and AUCs of 96.42% and 96.42%, respectively, outperforming conventional CNN, DNN, and SVM baselines. Ablation studies provide additional evidence of the performance improvements enabled by quantum Fourier transform and entanglement layers. Such results suggest that quantum–classical learning can efficiently improve feature extraction and discrimination in medical imaging, thus providing a modular and scalable route towards quantum–inspired clinical decision–support systems of the future.