This paper explores the potential of quantum ma-chine learning for breast cancer detection. We designed a binary classification approach using the BreastMNIST dataset and segmented mass regions derived from the BCDR dataset. A quanvolutional layer is employed as a quantum feature extractor, interfaced with elements of classical neural networks, to enhance the detection of malignant and benign patterns in breast tissue. The hybrid quanvolutional neural network aims to mitigate challenges associated with traditional machine learning models, such as feature sparsity and data imbalance. This architecture employs a simple yet efficient design that integrates the strengths of both quantum computing and classical methods, reducing computational complexity while maintaining performance. Re-sults demonstrate the potential of quanvolutions in diagnostic accuracy, offering a promising framework for integrating quan-tum computing in medical imaging. This approach provides an optimized solution that balances quantum processing with classical systems for more effective and scalable applications.
This paper explores the potential of quantum ma-chine learning for breast cancer detection. We designed a binary classification approach using the BreastMNIST dataset and segmented mass regions derived from the BCDR dataset. A quanvolutional layer is employed as a quantum feature extractor, interfaced with elements of classical neural networks, to enhance the detection of malignant and benign patterns in breast tissue. The hybrid quanvolutional neural network aims to mitigate challenges associated with traditional machine learning models, such as feature sparsity and data imbalance. This architecture employs a simple yet efficient design that integrates the strengths of both quantum computing and classical methods, reducing computational complexity while maintaining performance. Re-sults demonstrate the potential of quanvolutions in diagnostic accuracy, offering a promising framework for integrating quan-tum computing in medical imaging. This approach provides an optimized solution that balances quantum processing with classical systems for more effective and scalable applications.
Early diagnosis of breast cancer is crucial to combat its severity. Yet, it’s a challenging endeavor to tackle via traditional diagnosis methods. Several machine learning architectures have already been proposed to assist medical experts in diagnosis. However, architectural complexity hinders the goal of developing a cost-effective diagnosis model. Recently, quantum computing has become popular in machine learning due to its potential computational benefits. Although quantum computing has yet to show any significant improvement in machine learning due to its limited resources and complexity, it may reduce the computational complexity of machine learning in the future. Therefore, we proposed a novel hybrid classical-quantum machine learning model for breast cancer prediction. We collected the BreastMNIST dataset of 780 benign and malignant full-image mammogram samples. Our task is to create an efficient breast cancer-classifying hybrid machine learning model. The proposed method employs a modified ResNet50 as a feature extractor and a Quantum Neural Network to train on that feature and generate desired predictions. A Convolutional layer is applied as a dimension-reduction technique for features generated through ResNet50. Our architecture proved superior to other state-of-the-art hybrid methods, achieving an accuracy of 69% against 67%. An in-depth comparative analysis has been performed that shows the novelty of our research. This study paves the path for hybrid classical-quantum machine learning research in medical image classification.
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.
Quantum machine learning holds the potential to revolutionize cancer treatment and diagnostic imaging by uncovering complex patterns beyond the reach of classical methods. This study explores the effectiveness of quantum convolutional layers in classifying ultrasound breast images for cancer detection. By encoding classical data into quantum states through angle embedding and employing a robustly entangled 9-qubit circuit design with an SU(4) gate, we developed a Quantum Convolutional Neural Network (QCNN) and compared it to a classical CNN of similar architecture. Our QCNN model, leveraging two quantum circuits as convolutional layers, achieved an impressive peak training accuracy of 76.66% and a validation accuracy of 87.17% at a learning rate of 1 × 10−2. In contrast, the classical CNN model attained a training accuracy of 77.52% and a validation accuracy of 83.33%. These compelling results highlight the potential of quantum circuits to serve as effective convolutional layers for feature extraction in image classification, especially with small datasets.
The World Health Organization states that early diagnosis is essential to increasing the cure rate for breast cancer, which poses a danger to women's health worldwide. However, the efficacy and cost limitations of conventional diagnostic techniques increase the possibility of misdiagnosis. In this work, we present a quantum hybrid classical convolutional neural network (QCCNN) based breast cancer diagnosis approach with the goal of utilizing quantum computing's high-dimensional data processing power and parallelism to increase diagnosis efficiency and accuracy. When working with large-scale and complicated datasets, classical convolutional neural network (CNN) and other machine learning techniques generally demand a large amount of computational resources and time. Their restricted capacity for generalization makes it challenging to maintain consistent performance across multiple data sets. To address these issues, this paper adds a quantum convolutional layer to the classical convolutional neural network to take advantage of quantum computing to improve learning efficiency and processing speed. Simulation experiments on three breast cancer datasets, GBSG, SEER and WDBC, validate the robustness and generalization of QCCNN and significantly outperform CNN and logistic regression models in classification accuracy. This study not only provides a novel method for breast cancer diagnosis but also achieves a breakthrough in breast cancer diagnosis and promotes the development of medical diagnostic technology.
Breast cancer diagnosis through thermographic image analysis remains a critical challenge in medical AI, with classical deep learning approaches facing limitations in complex thermal pattern classification tasks. This paper presents a novel Hybrid Quantum Neural Network (HQNN) architecture that integrates quantum computing principles with classical convolutional neural networks for enhanced breast cancer classification. Our approach employs parameterized quantum circuits with multi-head attention mechanisms for quantum-aware feature encoding, coupled with classical convolutional layers for comprehensive pattern recognition. The quantum component utilizes a 4qubit variational circuit with strongly entangling layers, while the classical component incorporates advanced attention mechanisms for feature fusion. Experimental validation on breast cancer thermographic data demonstrates substantial performance improvements over state-of-the-art classical architectures, with the quantum-enhanced approach exhibiting superior convergence dynamics and enhanced feature representation capabilities. Our findings provide evidence for quantum advantage in medical image classification through classical simulation, establishing a framework for quantum-classical hybrid systems in healthcare applications. The methodology addresses key challenges in quantum machine learning deployment while maintaining computational feasibility on near-term quantum devices.
Breast cancer is among the leading causes of mortality among women worldwide, emphasizing the urgent need for accurate and efficient diagnostic tools. Machine learning-based systems have been heavily investigated in this regard. Quantum computing has garnered considerable attention recently; however, few studies have focused on its application to this specific problem. In this work, we integrate quantum computing principles with classical neural networks (QCNNs) to enhance the detection accuracy and computational efficiency of breast cancer diagnosis using the MedMNIST dataset. The hybrid QCNN framework leverages the power of quantum computing to perform complex feature mapping and extraction while reducing the estimated circuit depth of the quantum circuit compared to pure quantum neural network schemes. The proposed model achieved an accuracy of over 84%, a sensitivity of 82%, and a specificity of 86.0%, outperforming the state-of-the-art model developed for the same purpose. The focus of the study is to improve the accuracy and generalizability of classifying complex mammograms, as opposed to current quantum machine learning models, for practical applications in healthcare. We believe this study will support advancing the prospect of AI-enabled medical diagnosis with state-of-the-art quantum computing principles.