Multiclass disease classification is a crucial task in medical diagnostics, but achieving high accuracy across diverse disease categories remains challenging. This paper presents a novel approach that integrates Quantum Convolutional Neural Networks (QCNN) with the ResNet architecture to enhance multiclass skin disease classification. The model utilizes quantum computing principles to improve feature extraction and classification performance. We evaluate the model on a dataset comprising 16 skin disease classes from the DermNet dataset, covering both common and rare conditions like acne, melanoma, Eczema, Seborrheic Keratoses, Tinea Ringworm, Bullous disease, Poison Ivy, Psoriasis, Vascular Tumors, etc. Experimental results demonstrate that our QCNN-ResNet model achieves a classification accuracy of 86%, significantly surpassing the 66% accuracy of traditional ResNet models. This improvement is attributed to the enhanced feature representation provided by quantum layers, which capture complex patterns in medical images more effectively. Furthermore, the model exhibits robustness to noisy data, maintaining high accuracy across varying image quality levels. These findings highlight the potential of quantumenhanced neural networks in advancing medical diagnostics. Future research will explore scaling the model to larger datasets and extending its application to other medical imaging domains.
Multiclass disease classification is a crucial task in medical diagnostics, but achieving high accuracy across diverse disease categories remains challenging. This paper presents a novel approach that integrates Quantum Convolutional Neural Networks (QCNN) with the ResNet architecture to enhance multiclass skin disease classification. The model utilizes quantum computing principles to improve feature extraction and classification performance. We evaluate the model on a dataset comprising 16 skin disease classes from the DermNet dataset, covering both common and rare conditions like acne, melanoma, Eczema, Seborrheic Keratoses, Tinea Ringworm, Bullous disease, Poison Ivy, Psoriasis, Vascular Tumors, etc. Experimental results demonstrate that our QCNN-ResNet model achieves a classification accuracy of 86%, significantly surpassing the 66% accuracy of traditional ResNet models. This improvement is attributed to the enhanced feature representation provided by quantum layers, which capture complex patterns in medical images more effectively. Furthermore, the model exhibits robustness to noisy data, maintaining high accuracy across varying image quality levels. These findings highlight the potential of quantumenhanced neural networks in advancing medical diagnostics. Future research will explore scaling the model to larger datasets and extending its application to other medical imaging domains.
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.
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
Brain tumor detection is a vital and challenging task in medical diagnosis, requiring highly precise and efficient methods to improve patient treatment outcomes. Traditional approaches rely on classical machine learning and deep learning techniques, which, while effective to a degree, often struggle with capturing the complex and high-dimensional patterns present in medical imaging data. A significant limitation of these conventional methods is their inability to process and analyze intricate features efficiently, which can impact diagnostic accuracy. To address these challenges, this work proposes a novel Quantum Convolutional Neural Network (QCNN) framework that combines quantum computing principles, via quantum circuits, with classical machine learning architectures implemented through tools like PyTorch and PennyLane. This hybrid approach enhances feature extraction and classification capabilities, leading to superior performance. Experimental results demonstrate that the QCNN model achieves an improved accuracy of 90 percent on the test dataset, surpassing traditional methods and showing promise for advancing brain tumor detection technology. This improvement is supported by consistent decreases in training and validation loss during model training, highlighting the robustness and efficacy of the quantum-enhanced model.
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.
Quantum Convolutional Neural Networks (QCNNs) have became a promising field for quantum advantage in feature extraction and classification of different datasets like Handwritten datasets, Fashion datasets, particularly useful for complex datasets like medical images. Medical image classification has emerged as a critical component in modern healthcare diagnostics, particularly for tumor detection and cancer diagnosis. Normally, Classical CNN is used for image classification but they may face certain challenges in achieving further improvements in accuracy, computational efficiency, processing time. Also, it becomes difficult to train for high-dimensional medical datasets and extracting complex feature representations. Therefore, In this proposal, Image classification through a Scalable and Resource efficient QCNN is proposed which integrates classical CNN with quantum computing. This review examines the current state of medical image classification for tumor detection, analyzes the transition from classical CNNs to quantum-based architectures, and explores the potential of QCNNs in revolutionizing medical diagnostics. This research includes QCNN architecture which will include selective feature encoding using encoding technique such as Z Feature map and quantum circuits for convolutional layers and pooling layers to handle multi-modal medical data (e.g., MRI and CT scans). We aim to achieve superior accuracy with reduced parameters over classical CNNs. This work advances quantum ML toward practical deployment in resource-limited healthcare settings.
Quantum machine learning (QML) presents a promising avenue for addressing complex classification challenges, yet its application in medical imaging remains largely unexplored. This work introduces a hybrid quantum classical framework designed to classify skin diseases—Chickenpox, Measles, Monkeypox, and Normal—using a 4 qubit quantum system. The framework integrates a Quantum Convolutional Neural Network (QCNN) for feature extraction and a Variational Quantum Classifier (VQC) for classification, processing a dataset of 770 images with significant class imbalance. To address this imbalance, we employ a class weighted loss function with weightswyi= [3.0, 3.5, 0.6, 0.4], ensuring balanced representation across all classes. The model is optimized using Simultaneous Perturbation Stochastic Approximation (SPSA) over 150 iterations, achieving a test accuracy of 70.13% on a subset of 154 images, which represents 20% of the dataset. This marks a substantial improvement over both a random baseline of 25% and a prior run that achieved 37.66% accuracy, which was skewed toward the majority class. Principal Component Analysis (PCA) reduces image features to a 4 dimensional representation, enabling compatibility with the quantum system. The QCNN extracts these features, while the VQC performs classification based on measurements from 1024 shots on the QASM simulator. To ensure practical reusability, the trained model—including quantum circuits, optimized parameters, and PCA transformations—is saved for future deployment. Validation on a separate set of 24 samples (6 per class) yields 70.83% accuracy (17 out of 24 correct predictions), demonstrating the model’s generalization capabilities. While the performance falls short of classical benchmarks, this study establishes the feasibility of QML with minimal qubits, paving the way for quantum driven diagnostics in resource constrained environments.