Breast cancer is the second most common type of cancer among women worldwide. About 12.9% of women born in the United States today are expected to develop breast cancer during their lifetimes, equating to a chance of 1 in 8. Early and accurate classification of breast cancer is a vital task in medical imaging, as it significantly affects treatment outcomes and patient survival rates. In this work, a hybrid classical-quantum deep learning model that combines quantum transfer learning using ResNet152 with a dressed quantum network employing Hadamard, CNOT, and Ry gates is proposed. This integration takes advantage of the advanced feature extraction capabilities of ResNet152 along with the computational efficiencies and potential advantages offered by quantum networks. Our model achieves a training accuracy of 90.42% and a validation accuracy of 89.90%, demonstrating the effectiveness of incorporating quantum computing into deep learning workflows. In comparison, the classical ResNet152 achieves a training accuracy of 87.23% and validation accuracy of 85.78%, while ResNet50 reaches 86.89% training and 86.76% validation accuracy. These results underscore the potential of hybrid classical-quantum approaches in the advancement of medical diagnostics and highlight a promising direction to improve performance in complex classification tasks.
Breast cancer is the second most common type of cancer among women worldwide. About 12.9% of women born in the United States today are expected to develop breast cancer during their lifetimes, equating to a chance of 1 in 8. Early and accurate classification of breast cancer is a vital task in medical imaging, as it significantly affects treatment outcomes and patient survival rates. In this work, a hybrid classical-quantum deep learning model that combines quantum transfer learning using ResNet152 with a dressed quantum network employing Hadamard, CNOT, and Ry gates is proposed. This integration takes advantage of the advanced feature extraction capabilities of ResNet152 along with the computational efficiencies and potential advantages offered by quantum networks. Our model achieves a training accuracy of 90.42% and a validation accuracy of 89.90%, demonstrating the effectiveness of incorporating quantum computing into deep learning workflows. In comparison, the classical ResNet152 achieves a training accuracy of 87.23% and validation accuracy of 85.78%, while ResNet50 reaches 86.89% training and 86.76% validation accuracy. These results underscore the potential of hybrid classical-quantum approaches in the advancement of medical diagnostics and highlight a promising direction to improve performance in complex classification tasks.
Breast cancer remains a critical global health challenge, underscoring the need for early and precise diagnostic methodologies. This paper proposes a novel hybrid deep learning framework that integrates Quantum Neural Networks (QNNs) with the ResNet152 architecture and Transfer Learning to enhance breast cancer classification from medical images. Leveraging the feature extraction power of a pre-trained ResNet152 model, the system encodes refined feature representations into a parameterized quantum circuit utilizing Hadamard, CNOT, and Ry gates. Quantum error correction strategies such as Shor’s Code and surface code are incorporated to preserve quantum state fidelity and reduce decoherence-induced errors, thereby enhancing model robustness. Furthermore, quantum transfer learning accelerates training convergence and enriches pattern recognition capability in high-dimensional medical data. The proposed hybrid model achieves a classification accuracy of 97% and a real-time inference rate of 0.032 frames per second when deployed on an AI edge device, validating its efficacy for point-of-care diagnostics. This study underscores the transformative potential of synergizing quantum computing, edge AI, and deep learning to deliver scalable, interpretable, and high-precision diagnostic tools for clinical applications. The promising results open new avenues for integrating quantum technologies into conventional neural architectures to address the challenges of medical imaging and diagnostic intelligence.
One of the common and maybe fatal diseases harming women all around, breast cancer is one such condition. Accurate medical image analysis allows for early detection, which can greatly increase survival rates. Although they encounter challenges such as high computational expenses, poor data efficiency, and suboptimal feature extraction, conventional deep learning methods have produced promising results. By using quantum parallelism and entanglement to enable more quick and more complex computations, quantum computing provides a new approach to overcome these limitations. This paper intends to propose a hybrid quantum-classical deep learning model for the identification of breast cancer areas in mammographic images. The proposed method combines a quantum convolutional neural network (QCNN) with a classical convolutional neural network (CNN) to benefit from the advantages accompanying both classical and quantum processing. The hybrid model processes the mammogram data using classical pre-processing and quantum convolution layers; classification then follows with fully connected layers followed by classical pooling. The results of the MIAS dataset experiments indicate a classification accuracy of 98.4%, which is superior to the operation of traditional CNN and transfer learning-based models.
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.
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 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 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.