The paper applies quantum transfer learning for breast cancer detection by working on a dataset comprising 10,000 mammographic images of different demographics. With the assistance of balanced distribution between cases with and without cancer, the proposed quantum model succeeded in making improvements to great heights compared to CNN in the accuracy of diagnostics, sensitivity, and specificity. The model revealed high robustness, minimized false positive and false negative rates, and increased precision, as shown by AUC scores. The evidence here points to quantum computation as the future for improved early detection of breast cancers, hence timely diagnosis leading to better patient outcomes. The study also had apparent ethical and data protection aspects, where anonymized data is used and safe storage to ensure that information about patients is not shared. Although promising, the results must be validated on real-world clinical data sets and across multiple imaging modalities before confirming clinical utility. This work represents the transformative role quantum transfer learning could play in advancing medical imaging technology and diagnostic capabilities.
The paper applies quantum transfer learning for breast cancer detection by working on a dataset comprising 10,000 mammographic images of different demographics. With the assistance of balanced distribution between cases with and without cancer, the proposed quantum model succeeded in making improvements to great heights compared to CNN in the accuracy of diagnostics, sensitivity, and specificity. The model revealed high robustness, minimized false positive and false negative rates, and increased precision, as shown by AUC scores. The evidence here points to quantum computation as the future for improved early detection of breast cancers, hence timely diagnosis leading to better patient outcomes. The study also had apparent ethical and data protection aspects, where anonymized data is used and safe storage to ensure that information about patients is not shared. Although promising, the results must be validated on real-world clinical data sets and across multiple imaging modalities before confirming clinical utility. This work represents the transformative role quantum transfer learning could play in advancing medical imaging technology and diagnostic capabilities.
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.
The need to have rapid technological solutions is becoming more evident as the information world digitizes and the amount of data around us grows.It is argued that the future of computational systems will be quantum computing, which will be faster and more capable of solving several issues that cannot be solved with current computers.The aim of the current research is to examine the contributions made by quantum transfer learning models in enhancing the detection of breast cancer.Although both classical and deep learning approaches have proved to be effective, they continue to face serious challenges in handling high-dimensional and complex medical data.Quantum computing can be a feasible solution to such complexity.This study presents a hybrid model, which combines a classical pre-trained deep learning model ( ResNet50) with a range of variational quantum circuits-simple, entangled, and more complex.The main goal is to find the best model set up with regard to predictive power and computation time.It has been found that a hybrid model with an entangled variational circuit has an accuracy of 98.46, a precision of 100, and an F1-score of 97.3, which means a better result compared to the standard transfer learning model, which had an accuracy of 94.6, a precision of 97, and an F1-score of 90.4.These results improve upon recent quantum transfer learning studies, such as Azevedo et al. (2022) with 84% accuracy, and align closely with state-of-the-art quantumoptimized models reporting up to 99.3% accuracy, highlighting the effectiveness of our entangled variational approach on ultrasound images.Meanwhile, the traditional transfer learning model was the best in computational resource utilization.
Breast cancer is one of the most common neoplasms among women around the world, and early diagnosis significantly improves survival rates. Conventional techniques in the field of machine learning have shown great promise in the prediction of cancer, considering the fact that there is always an opportunity for advancement in terms of computing speed and scalability, especially when working with huge, high-dimensional data sets. In recent years, quantum computing (QC) has seemed to be becoming more prevalent as a powerful alternative. This study investigates the efficiency of traditional and quantum machine learning models in distinguishing between benign and malignant breast cancer tumors. The quantum model has achieved better balance in terms of precision and recall, especially in the case of malignant instance detection. These results demonstrate how QC can be used to supplement conventional machine learning techniques for improved predictive performance in breast cancer diagnosis, marking a significant step toward utilizing the technology’s potential in medicinal applications.
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.
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.
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.