Abstract The U-Net architecture is widely recognized as one of the most prominent models for medicalimage segmentation, comprising an encoder and a decoder. The encoder is crucial for extractingimage features to enhance segmentation accuracy, typically incorporating convolutional and poolinglayers. However, standard encoder structures often miss some classical features and struggleto extract high-level features effectively. In this paper, a hybird Quantum-Classical Neural Network(QC-Net) model with a novel encoder is proposed, aiming to capture more representativefeatures. Our encoder features a Residual Convolutional Block (RCB) to primarily extract somemissed features, and then Efficient Channel Attention (ECA) is employed into the output featuremaps after the RCB and pooling operations to handle more complex and noisy information. Consequently,a two-qubit parameterized circuit is devised to capture the final output features of theencoder, aiming to further capture the hidden high-level features from the quantum dimension.The decoder incorporates a joint attention mechanism and deconvolution operations to recoverthe spatial resolution and detail information of the original input image. To validate its efficacy,we conduct skin lesion segmentation experiments utilizing the ISIC2018 dataset. Notably, ourQC-Net model outperforms both U-Net and CA-Net, achieving an average Dice coefficient of93.2%, indicating improvements of 5.43% and 1.12%, respectively. These results underscore theoutstanding performance of our proposed QC-Net model.
Abstract The U-Net architecture is widely recognized as one of the most prominent models for medicalimage segmentation, comprising an encoder and a decoder. The encoder is crucial for extractingimage features to enhance segmentation accuracy, typically incorporating convolutional and poolinglayers. However, standard encoder structures often miss some classical features and struggleto extract high-level features effectively. In this paper, a hybird Quantum-Classical Neural Network(QC-Net) model with a novel encoder is proposed, aiming to capture more representativefeatures. Our encoder features a Residual Convolutional Block (RCB) to primarily extract somemissed features, and then Efficient Channel Attention (ECA) is employed into the output featuremaps after the RCB and pooling operations to handle more complex and noisy information. Consequently,a two-qubit parameterized circuit is devised to capture the final output features of theencoder, aiming to further capture the hidden high-level features from the quantum dimension.The decoder incorporates a joint attention mechanism and deconvolution operations to recoverthe spatial resolution and detail information of the original input image. To validate its efficacy,we conduct skin lesion segmentation experiments utilizing the ISIC2018 dataset. Notably, ourQC-Net model outperforms both U-Net and CA-Net, achieving an average Dice coefficient of93.2%, indicating improvements of 5.43% and 1.12%, respectively. These results underscore theoutstanding performance of our proposed QC-Net model.
Magnetic resonance imaging (MRI) brain tumor segmentation is critical for diagnosis and treatment planning, but remains challenging due to blurry boundaries and class imbalance. Although deep learning models like U-Net have set benchmarks, they often face limitations in modeling complex feature interactions and require substantial computational resources. This paper introduces Quantum U-Net, a novel hybrid quantum-classical architecture that augments the classical U-Net by replacing its final convolutional layer with a tailored quantum convolutional layer. This layer utilizes amplitude encoding to map 64-channel feature maps onto the state of 6 qubits, followed by a parameterized strongly entangling layer to exploit quantum superposition and entanglement for enhanced multi-channel feature fusion. We employ a two-stage training strategy: pre-training a classical U-Net for 50 epochs, freezing its parameters, and then fine-tuning the integrated quantum layer for 30 epochs. Evaluated on the Brain Tumor Segmentation dataset, our hybrid model achieves a Dice coefficient of 0.806, a significant 6.2% relative improvement over the baseline classical U-Net (Dice: 0.759). This work validates the potential of quantum-enhanced models to improve performance in medical image segmentation and provides a practical blueprint for hybrid quantum-classical architectures in resource-intensive domains.
The characterization of brain tumors from magnetic resonance imaging (MRI) is crucial for diagnosis, treatment planning, and prognosis. But the analysis of brain tumors based on MRI is still difficult because of the significant intra-tumoral heterogeneity, ambiguous boundaries of lesions, high dimensionality of imaging features, and complicated nonlinear relationships between anatomical structures. While transformer-based deep learning (DL) models have greatly advanced automated tumor analysis, classical feature representations often fail to effectively capture complex feature interactions in pathological images, especially when distinguishing between subtle pathological variations and normal tissue needs to be done efficiently. Recent quantum machine learning (QML) developments indicate that quantum feature encoding can map complex data to exponentially larger Hilbert spaces, leading to a more expressive representation of nonlinear patterns and feature correlations that are challenging to model with conventional architectures. Encouraged by this potential, this study proposes a hybrid quantum-classical framework, named QFormer-Brain, which combines transformer-based segmentation with quantum representation learning for automated brain tumor segmentation and classification. To segment the tumor regions, a Shifted Window UNet Transformers (Swin-UNETR) architecture is first used to capture both local anatomical details and global contextual information through hierarchical self-attention. Then, the deep semantic features of the segmented lesions are mapped to the quantum feature mapping module with angle encoding and variational quantum circuits (VQCs), which can obtain richer nonlinear feature representation by quantum superposition and entanglement mechanisms. The obtained quantum embeddings are then combined with features obtained from the transformer and classified by a Quantum Transformer Classifier. The experiments were performed on the publicly available BraTS 2021 data set using an 8-qubit variational quantum circuit, with 1024 measurement shots and readout-error mitigation, via the IBM Qiskit Aer simulator. The proposed framework achieved a Dice Similarity Coefficient (DSC) of 97.1%, Intersection-over-Union (IoU) of 95.0%, classification accuracy of 98.5%, sensitivity of 98.1%, specificity of 99.0%, and F1-score of 98.3%, outperforming U-Net, nnU-Net, ResNet50, Vision Transformer (ViT), and conventional hybrid Convolutional Neural Network (CNN)-based models. These results show that quantum-inspired representation learning can be efficiently integrated into transformer-based models to boost the discriminative feature extraction from complex MRI data.
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.
Skin cancer is among the most widespread and life-threatening conditions in the world with the need to detect lesions promptly and consistently in order to treat the disease in a timely manner. Nonetheless, automated lesion classification of the skin is a complex challenge due to inter-class similarity, which is high, illumination variation, and imbalance in the data in various classes of lesions. To address these obstacles, this paper presents a lightweight but powerful 6-qubit Hybrid Quantum-Classical Convolutional Neural Network (HQNN) that is optimized to 6-bit (low-precision) medical imaging applications. The proposed framework will use a ResNet-18 backbone to learn deep dermatological features based on dermoscopic images and a Particle Swarm Optimization (PSO) algorithm with a Random Forest (RF) ranking of features will reduce the 512-dimensional embedding space into six highly discriminative descriptors, which can be encoded in quantum. These are then converted into quantum states with ZZFeatureMap and RealAmplitudes ansatz which comprise a 6-qubit variational circuit the output of which is fused with a classical CNN classifier to achieve final lesion prediction. This strategy of optimization of quantum-aware features is a balance between the representation of high dimensions and limitations of qubits, which ensures a computational tractability on near-term noisy intermediate-scale quantum (NISQ) devices. Existing experimental results on the ISIC 2019 skin-lesion dataset show state-of-the-art performance achieving 98.2% accuracy, 98.5% precision, 97.7% recall, 98.0% F1-score, and a macro ROC-AUC of 0.985. The findings affirm that quantized deep learning with low-qubit quantum encoding has better diagnostic performance, noise resistance, and scalability with minimal hardware costs, making Q-SkinNet a realistic advancement towards experimental quantum-assisted dermatological diagnostics.
Brain tumor detection from MRI images is a critical and challenging task in medical diagnostics, where early identification greatly improves treatment outcomes. This paper presents a Hybrid Quantum– Classical Convolutional Neural Network (HQCNN) that integrates the strengths of ResNet-18 and a Variational Quantum Circuit for efficient and accurate tumor classification. The proposed model first employs ResNet-18 as a classical feature extractor, where 17 convolutional layers and pooling operations capture both low-level and high-level spatial features from MRI images. The final fully connected layer of ResNet-18 is replaced by a quantum processing layer implemented using PennyLane. In this layer, the extracted classical features are encoded onto qubits using RY rotation gates, while CNOT gates introduce entanglement between them, forming a VQC with trainable parameters. After quantum computation, Pauli-Z measurements convert the processed quantum states back into classical values, which are passed to a final fully connected layer for binary classification as Tumor or No Tumor. The hybrid integration of classical CNN feature learning with quantum entanglement– based representation enhances model generalization and interpretability. Experimental analysis demonstrates that the proposed HQCNN achieved 94.65% accuracy, outperforming traditional CNN models and confirming the potential of hybrid quantum–classical architectures for reliable and interpretable medical image diagnosis.
Abstract Quantum-classical hybrid neural networks have recently emerged as a prominent research direction, combining the expressive power of quantum computing with the robustness and scalability of classical deep learning. These models have demonstrated significant potential, especially in image classification scenarios. However, existing hybrid architectures often fail to jointly model spatial and channel-wise features, which may constrain their representational capacity. In this work, we propose a novel quantum-enhanced convolutional neural network architecture that integrates Quantum Positional Encoding (QPE) and Quantum Channel Attention (QCA) mechanisms. These components respectively enhance spatial and channel-wise feature representations, while ensuring compatibility with conventional training pipelines. Experiments on the MNIST and Fashion-MNIST datasets demonstrate that the proposed model consistently outperforms classical CNN baselines in classification accuracy. Furthermore, experiments on the challenging CIFAR-10 dataset further validate the competitive performance of the proposed model, highlighting its strong generalization capability. These findings underscore the practical value of integrating quantum mechanisms into deep learning and emphasize their potential to advance intelligent visual recognition systems.