Medical image classification is an important task in clinical decision support systems. Although classical deep learning models can represent massive amounts of data, they are not very scalable DEEP LEARNING REVIEW and stand each in their own right for privacy loss and computation inefficiency. Quantum machine learning (QML) has been proposed as an exciting field to complement classical machine learning by using quantum superposition and entanglement for increasing the expressibility of the models. Since the hybrid quantum-classical methods are subject to high qubit complexity [10], training instability (barren plateau problem) [13, 14], non-existent privacy protection mechanisms and poor generalisability over trained datasets [7]. Herein, we propose AF-HQCNN: an Adaptive Federated Hybrid Quantum Convolutional Neural Network (1) a lightweight classical feature extractor using only MobileNetV2, (2) variational quantum circuit (VQC) with angle and amplitude encoding with 4-8 qubits, (3) novel adaptive momentum-based quantum optimizer to mitigate gradient vanishing and gradient explosion issues, (4) federated learning aggregation layer layer to ensure protection of patient data privacy,(5), multi-dataset validation on MedMNIST, HAM10000, Brain MRI & Chest X-Ray datasets. Our experimental results show that AF-HQCNN leads to the mean classification accuracy of 97.6%, outperforming existing hybrid models by a margin of 2.7% with 87% fewer trainable parameters, faster convergence and robustness against privacy attacks.
Medical image classification is an important task in clinical decision support systems. Although classical deep learning models can represent massive amounts of data, they are not very scalable DEEP LEARNING REVIEW and stand each in their own right for privacy loss and computation inefficiency. Quantum machine learning (QML) has been proposed as an exciting field to complement classical machine learning by using quantum superposition and entanglement for increasing the expressibility of the models. Since the hybrid quantum-classical methods are subject to high qubit complexity [10], training instability (barren plateau problem) [13, 14], non-existent privacy protection mechanisms and poor generalisability over trained datasets [7]. Herein, we propose AF-HQCNN: an Adaptive Federated Hybrid Quantum Convolutional Neural Network (1) a lightweight classical feature extractor using only MobileNetV2, (2) variational quantum circuit (VQC) with angle and amplitude encoding with 4-8 qubits, (3) novel adaptive momentum-based quantum optimizer to mitigate gradient vanishing and gradient explosion issues, (4) federated learning aggregation layer layer to ensure protection of patient data privacy,(5), multi-dataset validation on MedMNIST, HAM10000, Brain MRI & Chest X-Ray datasets. Our experimental results show that AF-HQCNN leads to the mean classification accuracy of 97.6%, outperforming existing hybrid models by a margin of 2.7% with 87% fewer trainable parameters, faster convergence and robustness against privacy attacks.
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 poses significant challenges due to the complex and nonlinear patterns present in imaging data. Traditional deep learning models often struggle to capture the intricate relationships inherent in such data, especially under conditions of noise and training instability. To address these limitations, this study proposes a novel Hybrid Quantum Convolutional Neural Network (HQCNN) integrated with a Gradient Variance-Controlled Optimizer (GVCO). The aim is to enhance diagnostic accuracy by combining classical convolutional layers for shallow feature extraction with a parameterized quantum circuit (PQC) that enables richer and more discriminative feature representations. The GVCO dynamically adjusts learning rates and momentum coefficients based on real-time gradient variance, improving convergence speed and generalization performance. The proposed HQCNN-GVCO model was evaluated on two public brain imaging datasets: the Kaggle Brain MRI dataset and the REMBRANDT dataset. It achieved classification accuracies of 99.2% and 98.2%, respectively. Compared to baseline HQCNN and other state-of-the-art models, HQCNN-GVCO demonstrated superior and more consistent performance. These findings highlight the potential of integrating quantum computing techniques with adaptive optimization strategies to advance reliable and accurate medical image analysis.
Classification of medical images plays a vital role in medical image analysis; however, it remains challenging due to the limited availability of labeled data, class imbalances, and the complexity of medical patterns. To overcome these challenges, we propose a novel Hybrid Quantum-Classical Neural Network (HQCNN) for both binary and multi-class classification. The architecture of HQCNN integrates a five-layer classical convolutional backbone with a 4-qubit variational quantum circuit that incorporates quantum state encoding, superpositional entanglement, and a Fourier-inspired quantum attention mechanism. We evaluate the model on six MedMNIST v2 benchmark datasets. The HQCNN consistently outperforms classical and quantum baselines, achieving up to 99.91% accuracy and 100.00% AUC on PathMNIST (binary) and 99.95% accuracy on OrganAMNIST (multi-class) with strong robustness on noisy datasets like BreastMNIST (87.18% accuracy). The model demonstrates superior generalization capability and computational efficiency, accomplished with significantly fewer trainable parameters, making it suitable for data-scarce scenarios. Our findings provide strong empirical evidence that hybrid quantum-classical models can advance medical imaging tasks.
Alzheimer’s disease (AD) is a progressive neurological disorder and one of the most common types of dementia, causing impaired memory and quality of life. Therefore, an early and accurate diagnosis is essential for intervention and patient care. Convolutional Neural Networks (CNNs) and other deep learning models have demonstrated promise in medical imaging but may need centralized collection of data, which raises privacy concerns. In this study, we propose a Federated Hybrid Quantum Convolutional Neural Network (FH-QCNN) that integrates quantum computing with federated learning for privacy-preserving classification of Alzheimer’s disease through magnetic resonance imaging (MRI). This framework enables collaborative model training across multiple clients without sharing raw patient data. A Parametric Quantum Circuit (PQC) with 4- and 8-qubit configurations is incorporated into the CNN architecture to explore hybrid quantum-classical feature representations. On the evaluated Kaggle benchmark dataset, the 8-qubit FH-QCNN with three clients achieved slice-level classification accuracies of 99.69% for binary classification and 99.02% for multiclass classification. These results demonstrate the methodological feasibility of the proposed hybrid quantum federated framework under the evaluated benchmark protocol rather than its clinical performance. Additional evaluations under heterogeneous Non-Independent and Identically Distributed (non-IID) federated environments, reduced training data settings, and NISQ-inspired noisy quantum conditions demonstrated consistent performance under the evaluated simulation settings. Gradient-weighted Class Activation Mapping (Grad-CAM)-based explainability analysis further indicated that the FH-QCNN focused on anatomically relevant brain regions during prediction.
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.
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.