Cancer is one of the leading causes of death worldwide. Mutations in humans can alter oncogenes or tumor suppressor genes, leading to uncontrolled cell growth. The most common form of cancer is skin cancer which if caught early, is much easier to treat, highlighting the importance of a sophisticated skin cancer detection system. In this study, we propose a novel method to detect skin cancer early, utilizing quantum variational circuits to perform a quantum convolution as a pre-processing step on image data for classical Convolutional Neural Networks (CNN) to create a hybrid "Quanvolutional" Neural Network. Utilizing a dataset of over 8,100 images which included images of other skin conditions including Herpes Simplex, Burns, Xerosis, folliculitis, and more, our model obtained a validation accuracy of 99.30% for the classification of Skin Cancer. This performance surpasses that of similar, purely classical CNNs, despite the presence of noise from other skin conditions. Our study not only demonstrates significant potential for skin cancer detection, but also the efficiency and effectiveness of quantum pre-processing layers in hybrid Quanvolutional Neural Networks. Future work can utilize our Quantum Machine Learning methods, leading to further breakthroughs within the medical field and quantum computing as a whole.
Cancer is one of the leading causes of death worldwide. Mutations in humans can alter oncogenes or tumor suppressor genes, leading to uncontrolled cell growth. The most common form of cancer is skin cancer which if caught early, is much easier to treat, highlighting the importance of a sophisticated skin cancer detection system. In this study, we propose a novel method to detect skin cancer early, utilizing quantum variational circuits to perform a quantum convolution as a pre-processing step on image data for classical Convolutional Neural Networks (CNN) to create a hybrid "Quanvolutional" Neural Network. Utilizing a dataset of over 8,100 images which included images of other skin conditions including Herpes Simplex, Burns, Xerosis, folliculitis, and more, our model obtained a validation accuracy of 99.30% for the classification of Skin Cancer. This performance surpasses that of similar, purely classical CNNs, despite the presence of noise from other skin conditions. Our study not only demonstrates significant potential for skin cancer detection, but also the efficiency and effectiveness of quantum pre-processing layers in hybrid Quanvolutional Neural Networks. Future work can utilize our Quantum Machine Learning methods, leading to further breakthroughs within the medical field and quantum computing as a whole.
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 one of the most common and lethal diseases in America, and its early detection and treatment is the best approach to protect the lives of those afflicted. Computer-Aided Diagnosis systems have been implemented for decades as evidence of intelligent methods applied in the medical field. In recent years, machine and deep learning fields have shown great potential in medical diagnosis, particularly in skin cancer classification. These methods enable the automated extraction of complex input features, such as those in medical imagery. Therefore, with the advent of quantum computing, it is now possible to perform complex computations with increased speed and efficiency. This study explores the application of quantum machine learning in the classification of skin cancer using the ResNet50 model, a deep convolutional neural network for RGB images. The research employs a quantum-enhanced version of ResNet50 using a quanvolutional layer that the input skin lesion images go through and compare its performance with the classical ResNet50 version. We show comparative experiments, and the results indicate that further experiments are needed using more extensice datasets and different quantum deep learning architectures.
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.
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.
This study investigates the application of a Advanced Intelligent Quantum Convolutional Neural Network (AI-QCNN) for classifying skin cancer images using the HAM10000 dataset. By integrating quantum computing with traditional CNN techniques, the model aims to enhance both classification accuracy and processing efficiency. During preprocessing, images undergo normalization, resizing, and augmentation to improve data quality and diversity, which helps the model to generalize more effectively and mitigate overfitting. The AIQCNN architecture combines classical convolutional layers for initial feature extraction with quantum circuits that further refine these features through quantum entanglement, resulting in improved classification outcomes. The AIQCNN achieves a training accuracy of 96% and a validation accuracy of 84%, demonstrating faster convergence and greater stability compared to conventional CNNs. These results highlight the potential of quantum computing to advance medical image classification and suggest opportunities for optimizing down sampling techniques and applying the model to various other medical imaging tasks.
Breast cancer is one of the most prevalent and life-threatening diseases affecting women worldwide, and early detection plays a crucial role in improving survival rates and treatment outcomes.Conventional diagnostic methods such as mammography, ultrasound imaging, and histopathological analysis rely heavily on expert interpretation and classical machine learning techniques.Although classical deep learning models, especially Convolutional Neural Networks (CNNs), have demonstrated impressive performance in medical image analysis, they face inherent limitations related to computational complexity, scalability, and the curse of dimensionality when handling large, high-resolution datasets.With the rapid advancement of quantum computing, Quantum Machine Learning (QML) has emerged as a promising paradigm capable of addressing these challenges by leveraging quantum phenomena such as superposition and entanglement.This research explores the application of Quantum Convolutional Neural Networks (Quantum CNNs or QCNNs) for breast cancer detection, aiming to enhance classification accuracy while reducing computational overhead.The proposed approach integrates quantum circuits with classical preprocessing techniques to extract discriminative features from breast cancer datasets, such as mammographic or histopathological images.By encoding image features into quantum states and performing convolution operations through parameterized quantum circuits, the Quantum CNN model is designed to learn complex patterns more efficiently than classical counterparts.The study evaluates the effectiveness of Quantum CNNs in distinguishing between benign and malignant breast tumors and compares its performance with traditional CNN models.The findings highlight the potential of Quantum CNNs to revolutionize medical image analysis by offering faster convergence, improved generalization, and robustness against noise.This research contributes to the growing field of quantumassisted healthcare systems and demonstrates how quantum computing can be harnessed to address critical challenges in breast cancer diagnosis.