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.
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.
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.
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 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.
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.
Cancer is the leading cause of premature mortality. Globally, the prevalence of cancer is on the rise, and early-stage diagnosis is crucial for cancer recovery and survival. However, the presence of circulating tumor cells (CTCs) and heterogeneity within the tumor makes it hard to track how cancer changes over time and how resistance to treatment develops. Unfortunately, the cancer diagnosis precision remains inadequate, endangering precious human lives and necessitating costly procedures for accurate treatment. Machine learning (ML) has proven capable of discovering meaningful information and making predictions, particularly with large datasets. Moreover, quantum computers (QCs) inherently possess superposition and parallel computation, features not found in classical computing. Researchers have demonstrated that quantum neural networks (QNNs) achieve a significantly better effective dimension than classical NNs. In this context, this study investigates the application of classical and quantum machine learning (QML) algorithms to classify breast cancer images. Region-based segmentation is used to identify regions of interest and image processing algorithms are performed on segmented breast cancer images to extract meaningful features (e.g., perimeter, area, compactness). An user interface (UI) based software is created to swiftly distinguish benign and malignant instances during image analysis. Although QNNs perform poorly than the classical NNs, there is still a long way to go in developing feature maps and techniques for better performance. In contrast, the classical NNs have gone a long way with well-established algorithms and packages available to us. This research shows that with further development, QML has the potential to enhance cancer diagnosis accuracy and speed, ultimately improving early detection and treatment 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.