Skin cancer identification and categorization are still open issues in medical image analysis due to ambiguous boundaries, variance in shapes, and strong similarities to non-malignant lesions, which must be acknowledged by any framework supporting the clinical decision making. The proposed Quantum-Enhanced Deep Learning Framework (QDLF) is a new approach that combines Quantum feature encoding and Classical deep learning networks for skin lesion classification. Hereby, the QDLF is compared with the traditional models such as ResNet50, DenseNet, and VGG-16 showing a better performance on the HAM10000 dataset containing 10,015 dermoscopic images with seven lesion categories. Therefore, higher accuracy was achieved in the proposed framework at an accuracy level of 96.2% with F1-score 95.1% and ROCAUC value of 0.983 as compared to classical approaches. The QDLF uses quantum feature mappings with Variational Quantum Circuits (VQCs) that enables the model to learn abstract non-linear patterns and it incorporates global contextual features from pre-trained CNNs. The use of a mixture of CNN and FNN in this study not only improves the classification efficiency but also cuts down the time taken to train and the number of parameters involved, getting to a convergence point in 36 minutes with 18.5 million of parameters. To further enhance the interpretability of the model, Grad-CAM visualization is employed to identify clinically significant areas of the lesions as well as t-SNE plots showing good distinction in the quantum features space. The findings affirm the effectiveness of QDLF as a method for addressing class imbalance issues, with precision and recall for key classes such as melanoma at 92.7% and 94.1% respectively. Quantum-classical hybrid frameworks presented in this work reveal the uniqueness of the approach to medical image analysis in terms of scalability and applicability to real-life cases. Further studies will focus on implementation on quantum platforms and generalization of this approach to other image processing tasks in medicine.
Skin cancer identification and categorization are still open issues in medical image analysis due to ambiguous boundaries, variance in shapes, and strong similarities to non-malignant lesions, which must be acknowledged by any framework supporting the clinical decision making. The proposed Quantum-Enhanced Deep Learning Framework (QDLF) is a new approach that combines Quantum feature encoding and Classical deep learning networks for skin lesion classification. Hereby, the QDLF is compared with the traditional models such as ResNet50, DenseNet, and VGG-16 showing a better performance on the HAM10000 dataset containing 10,015 dermoscopic images with seven lesion categories. Therefore, higher accuracy was achieved in the proposed framework at an accuracy level of 96.2% with F1-score 95.1% and ROCAUC value of 0.983 as compared to classical approaches. The QDLF uses quantum feature mappings with Variational Quantum Circuits (VQCs) that enables the model to learn abstract non-linear patterns and it incorporates global contextual features from pre-trained CNNs. The use of a mixture of CNN and FNN in this study not only improves the classification efficiency but also cuts down the time taken to train and the number of parameters involved, getting to a convergence point in 36 minutes with 18.5 million of parameters. To further enhance the interpretability of the model, Grad-CAM visualization is employed to identify clinically significant areas of the lesions as well as t-SNE plots showing good distinction in the quantum features space. The findings affirm the effectiveness of QDLF as a method for addressing class imbalance issues, with precision and recall for key classes such as melanoma at 92.7% and 94.1% respectively. Quantum-classical hybrid frameworks presented in this work reveal the uniqueness of the approach to medical image analysis in terms of scalability and applicability to real-life cases. Further studies will focus on implementation on quantum platforms and generalization of this approach to other image processing tasks in medicine.
Skin cancer, in particular melanoma, is still one of the most significant contributing factors to cancer-related deaths globally, indicating the urgency of reliable diagnostic tools. Early discovery of skin lesions can radically increase patient survival rates, but manual diagnosis is not an easy task, and it is time-consuming as well. This paper uses Quantum Convolutional Neural Networks (QCNN), a hybrid quantum-classical model for the skin lesion classification task as being benign or malignant. The model intertwines quantum feature extraction using an amplitude encoding and parameterized quantum circuit with classical convolutional neural network (CNN) layers analyzing spatial features. Advanced pre-processing techniques and Synthetic Minority Over-sampling Techniques (SMOTE) address the class imbalance, as federated learning ensures decentralized nodes’ privacy-preserving training. The explainability of the model predictions is heightened through Quantum SHAP (Shapley Additive Explanations) and Gradient-Weighted Class Activation Mapping (Grad-CAM). This work evaluates QCNN on the Melanoma Skin Cancer Dataset of 10,600 images, achieving 96.87% training accuracy and a validation accuracy of 85% while surpassing classical benchmarks in accuracy, efficiency, and robustness. Therefore, this quantum-classical model has highlighted potential applications of medical image diagnostics through quantum-classical models
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.
Skin cancer is the most common and fatal illness globally, and therefore, proper early detection is essential for successful treatment and enhanced patient outcomes. Classic deep learning models, especially Convolutional Neural Networks (CNNs), have greatly succeeded in medical image classification. However, classic CNNs also suffer from severe limitations such as computational inefficiency, overfitting on small datasets, and redundant feature extraction, which restrict their utility in clinical settings. To overcome these drawbacks, we introduce QAttn-CNN as a quantum–classical deep learning model combining a Quantum Attention Mechanism (QAttn) to improve feature selection and classification accuracy. Our method utilizes Quantum Convolutional Layers (QConv) and Quantum Image Representation (QIR) with Novel Enhanced Quantum Representation (NEQR) encoding to draw upon quantum parallelism and improve computational efficiency and complexity from O(N 2 ) to O(log N). The model is tested on three benchmark datasets: MNIST (70,000 grey-scale handwritten digit images), CIFAR-10 (60,000 RGB object images), and the Kaggle Skin Cancer: Malignant versus Benign dataset (3297 dermoscopic images: 1800 benign and 1497 malignant cases, from the International Skin Imaging Collaboration (ISIC) Archive). The dataset images were processed by converting them to grayscale, resizing them bilinearly to 150 × 150 pixels, and normalizing to [0–1] for quantum encoding. QAttn-CNN is contrasted with standard CNNs, QAttn-ViT (Quantum Attention Vision Transformer), and QAttn-ResNet18. Results indicate that QAttn-CNN attains state-of-the-art accuracy of 91% on the Skin Cancer dataset with a precision of 89%, a recall of 89%, and an F1-score of 91%, surpassing Baseline CNN (89% accuracy), QAttn-ViT (87%), and QAttn-ResNet18 (83%). On CIFAR-10, QAttn-CNN exhibits 10% accuracy enhancement over Baseline CNN with accuracy of 82% and 90% precision. On MNIST, QAttn-CNN performs at the peak of 99% accuracy, comparable to classical benchmarks but with greatly diminished computational overhead due to quantum parallelism. This study demonstrates the revolutionary potential of quantum-assisted deep learning in healthcare applications, especially for real-world binary medical image classification problems that identify malignant vs. benign skin lesions.
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.
Abstract Classical deep learning models often struggle to fully capture complex visual patterns and to generalize effectively when trained on limited or imbalanced medical imaging data. To address this limitation, this paper presents a novel Hybrid Quantum Deep Learning (Hybrid QDL) framework that leverages the strengths of both deep neural networks and quantum circuits for advanced medical image interpretation. The proposed model integrates a pre-trained EfficientNet-B4 as the feature extractor and a variational quantum circuit as the classification head, enabling joint learning of hierarchical and quantum-enhanced representations. The Hybrid QDL model is evaluated on three clinically relevant datasets: COVIDx (for 3-class COVID-19 diagnosis using chest X-rays), PneumoniaMNIST (for pneumonia detection), and OrganAMNIST (for multi-organ abdominal classification). The model achieves state-of-the-art results, with 98.48% accuracy and 98.33% F1-score on COVIDx, outperforming both classical and prior quantum baselines. On PneumoniaMNIST and OrganAMNIST, it reaches 98.28% and 95.27% accuracy, respectively, along with high macro F1-scores. Ablation studies confirm that increasing quantum circuit depth and entanglement enhances discriminative power, particularly in complex multi-class scenarios. These results demonstrate that the Hybrid QDL framework is a robust, scalable solution for automated medical diagnosis and holds significant promise for real-world clinical decision support and COVID-19 detection.
Skin diseases affect millions of people worldwide, leading to significant healthcare burdens and challenges in diagnosis and treatment. In the past few years, machine learning techniques have demonstrated potential in assisting dermatologists with diagnosing various skin conditions. As in, conventional machine learning algorithms might encounter challenges in handling the complexity and distinction of skin disease classification tasks, primarily because of the intricate nature of medical image data with its high dimensional properties. In this work, the main analysis is done based on exploring quantum machine learning models for skin disease classification. This approach blends with the aspects of quantum computing with the conventional machine learning techniques to push the boundaries of skin disease classification. This work harnesses the HAM10000 dataset, an extensive compilation of categorized images portraying common skin lesions, to train and assess the efficacy of the proposed methodologies. Quantum computing libraries such as PennyLane and Qiskit is used in this study. Using different combination of qubit rotation encoding and decoding using three types of Pauli gates such as Pauli X, Y and Z gates are implemented and compared using proposed Quanvolutional neural network. Features extracted using MobileNet pre-trained network is used to build Quantum support vector classifier. These quantum machine learning models are compared with some well-known pre-trained models such as Resnet50, Inception-Resnet, Densenet121, DenseNet201 and MobileNet. The combination of RY qubit rotation and PauliZ gate in quantum convolution layer in Quanvolutional neural network produced the optimal classification accuracy of 82.86% more than any other models included in this study. In contrast, Quantum Support Vector Classifier produced similar classification accuracy of 72.5% with respect to pre-trained models.