Breast cancer is the most diagnosed cancer among women worldwide. As early detection significantly improves survival rates, accurate and efficient imaging diagnostics play a critical role in clinical decision-making. Traditional machine learning (ML) methods have demonstrated promising capabilities in medical imaging tasks. However, they often require large datasets and substantial computational resources, which limit their adaptability in resource-constrained environments. Quantum-Inspired Machine Learning (QI-ML) has emerged as a promising paradigm that leverages principles of quantum mechanics, such as superposition and entanglement, within classical computing architectures. This approach aims to address challenges such as model complexity, memory limitations, and generalization to small datasets. In this review, we explore the application of QI-ML to breast cancer imaging across modalities, including mammography, ultrasound, and Magnetic Resonance Imaging, discussing key algorithmic strategies, advantages over classical ML, and existing gaps.
Breast cancer is the most diagnosed cancer among women worldwide. As early detection significantly improves survival rates, accurate and efficient imaging diagnostics play a critical role in clinical decision-making. Traditional machine learning (ML) methods have demonstrated promising capabilities in medical imaging tasks. However, they often require large datasets and substantial computational resources, which limit their adaptability in resource-constrained environments. Quantum-Inspired Machine Learning (QI-ML) has emerged as a promising paradigm that leverages principles of quantum mechanics, such as superposition and entanglement, within classical computing architectures. This approach aims to address challenges such as model complexity, memory limitations, and generalization to small datasets. In this review, we explore the application of QI-ML to breast cancer imaging across modalities, including mammography, ultrasound, and Magnetic Resonance Imaging, discussing key algorithmic strategies, advantages over classical ML, and existing gaps.
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.
Breast cancer continues to pose a formidable global health challenge, claiming countless lives each year and highlighting the urgent need for early and precise detection. Timely diagnosis is critical for improving treatment outcomes and survival rates, yet conventional screening and diagnostic methods often fall short in handling complex medical data efficiently. Over the past two decades, artificial intelligence (AI) and machine learning (ML) have revolutionized breast cancer detection by analyzing medical imaging, histopathology, and genomic profiles. Despite their success, these classical approaches are constrained by high computational costs, reliance on large annotated datasets, and reduced generalization when data is limited or heterogeneous. Quantum Machine Learning (QML) has emerged as a transformative paradigm, integrating quantum computing principles—such as superposition, entanglement, and quantum parallelism—with classical learning frameworks to overcome these limitations. This survey provides a comprehensive overview of QML applications in breast cancer detection by reviewing key methodologies and advancements. It begins with a discussion of the clinical profile, treatment approaches, and recent epidemiological trends of breast cancer. Subsequently, it examines traditional AI and ML techniques used in diagnostic modeling, followed by emerging QML frameworks that leverage quantum principles for enhanced learning and efficiency. The paper concludes by outlining current challenges in QML research, including hardware constraints, noise sensitivity, and the lack of standardized evaluation benchmarks.
Breast cancer continues to be among the most common causes of cancer death globally, with early and precise diagnosis playing a pivotal role in enhancing patient survival. Traditional machine learning (ML) techniques have shown great promise in the field of medical imaging and diagnosis; however, the emergence of quantum machine learning (QML) offers new avenues for the improvement of pattern discovery and diagnostic accuracy in high-dimensional medical data. This work describes a thorough benchmark comparison of quantum and classical machine learning methods for breast cancer diagnosis over the Wisconsin Diagnostic Breast Cancer data and mammographic image data. We compare variational quantum classifiers (VQC), quantum kernel methods (QKM), quantum convolutional neural networks (QCNNs), and hybrid quantum-classical neural structures with traditional classical baselines like support vector machines (SVM) and convolutional neural networks (CNN). QKM techniques perform better in high-dimensional feature spaces and better generalize on external validation sets. This implementation framework describes in depth how to develop, train, and deploy QML models in the clinical workflow, including optimization approaches, code structures, and deployment issues. The work demonstrates that it is possible to streamline breast cancer screening and diagnosis using QML with more accurate and more efficient solutions, which would lead to a huge impact on patient outcomes.
Cancer remains one of the leading causes of mortality worldwide, making early and accurate detection a critical challenge in medicine. Recent advances in quantum computing have opened new avenues for enhancing machine learning techniques in medical imaging and diagnosis. In this work, we apply quantum machine learning models, including Quantum Convolutional Neural Networks (QCNN), to the task of breast cancer detection using ultrasound images. We designed and implemented a hybrid pipeline combining classical preprocessing techniques with quantum-enhanced classification. Our experiments were conducted on real medical imaging datasets, and the results demonstrate the potential of quantum approaches to improve diagnostic accuracy over classical baselines. This research lies at the intersection of quantum computing, artificial intelligence, and medical image analysis, and highlights the promise of quantum-enhanced methods in addressing complex healthcare problems.
Breast cancer is one among the most common diseases that are encountered in the world and one of the principal causes of death among women, early detection being crucial to enhance survival rates but mammograms are an important tool that can save lives by finding breast cancer at its earliest stages but the manual evaluation is dependent on a radiologist's clear interpretation and analysis of which sometimes erroneously or misses the key findings. This research work deals with breast cancer detection from mammogram images and hence provides a solution for the issues identified by training Quantum Convolutional Neural Network (Quantum CNN). Quantum CNNs is recently being integrated with classical deep learning models in the form of Quantum CNN where quantum mechanical properties are used to support feature extraction and data handling. This research work trains the Quantum CNN using publicly available datasets, including the CBIS-DDSM Breast Cancer Image Dataset and the Breast Histopathology Images dataset. Key methods used include data preprocessing techniques such as Region of Interest (ROI) extraction, morphological operations, and quantum-enhanced convolutional layers for image classification. The proposed model demonstrates improved accuracy and computational efficiency over classical CNNs, making it a promising tool for more accurate and faster breast cancer detection.
The integration of quantum networks with machine learning (ML) algorithms and deep neural networks (DNNs) has the potential to revolutionize medical imaging and electrocardiogram (ECG) analysis, particularly in diagnosing heart conditions and breast cancer. By leveraging quantum computing's enhanced processing capabilities, medical practitioners can achieve unprecedented accuracy and speed in identifying anomalies in cardiac structures and functions, as well as in categorizing malignant lesions indicative of breast cancer.Furthermore, in heart disease and breast cancer pathology, quantum-enhanced ML algorithms analyze ultrasound, CT, and MRI imaging data to detect cirrhosis, fibrosis, and hepatocellular carcinoma, differentiating between healthy and diseased tissues for early intervention. This quantum-based implementation empowers clinicians with superior diagnostic accuracy, facilitating prompt treatment, improving patient outcomes, and advancing the field's capability to diagnose complex medical conditions.