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.
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.
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.
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.
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.
In medical image analysis, skin lesion classification is a crucial problem since early detection can greatly lower health risks. The possibility of using quantum machine learning to replace traditional deep learning models has been studied recently.This study is positioned as a comparative benchmarking analysis to empirically evaluate the practical performance of quantum-based learning models against established deep learning architectures under identical experimental conditions.In this work, a renowned research technique was used to first create a Quantum Neural Network (QNN) and a Quantum Support Vector Classifier (QSVC). Many conventional deep learning models were then employed for comparison because of simulator limitations and poor performance. To provide a fair experimental setting, EFAM-Net, MobileNetV3, ResNet18, and EfficientNet-B0 were trained and assessed using the same dataset. Classical deep learning models performed better than quantum-based models, which had a modest classification accuracy of 62-65 %. ResNet18 and EFAM-Net had validation accuracy of 83.62 % and 79.89 %, respectively, while EfficientNet-B0 had the greatest validation accuracy of 90.21 %, closely followed by MobileNetV3 with 90.15 %. These findings suggest that conventional deep learning models are now more appropriate for real-world medical image categorisation since they provide more accuracy and training stability than quantum-based methods.
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.
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.