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.
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.
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 has emerged as a promising approach to improve feature extraction and classification tasks in high-dimensional data domains such as medical imaging. In this work, we present a hybrid Quantum-Classical Convolutional Neural Network (QCNN) architecture designed for the binary classification of the BreastMNIST dataset, a standardized benchmark for distinguishing between benign and malignant breast tumors. Our architecture integrates classical convolutional feature extraction with two distinct quantum circuits: an amplitude-encoding variational quantum circuit (VQC) and an angle-encoding VQC circuit with circular entanglement, both implemented on four qubits. These circuits generate quantum feature embeddings that are fused with classical features to form a joint feature space, which is subsequently processed by a fully connected classifier. To ensure fairness, the hybrid QCNN is parameter-matched against a baseline classical CNN, allowing us to isolate the contribution of quantum layers. Both models are trained under identical conditions using the Adam optimizer and binary cross-entropy loss. Experimental evaluation in five independent runs demonstrates that the hybrid QCNN achieves statistically significant improvements in classification accuracy compared to the classical CNN, as validated by a one-sided Wilcoxon signed rank test (p = 0.03125) and supported by large effect size of Cohen's d = 2.14. Our results indicate that hybrid QCNN architectures can leverage entanglement and quantum feature fusion to enhance medical image classification tasks. This work establishes a statistical validation framework for assessing hybrid quantum models in biomedical applications and highlights pathways for scaling to larger datasets and deployment on near-term quantum hardware.
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.
Quantum Convolutional Neural Networks (QCNNs) have became a promising field for quantum advantage in feature extraction and classification of different datasets like Handwritten datasets, Fashion datasets, particularly useful for complex datasets like medical images. Medical image classification has emerged as a critical component in modern healthcare diagnostics, particularly for tumor detection and cancer diagnosis. Normally, Classical CNN is used for image classification but they may face certain challenges in achieving further improvements in accuracy, computational efficiency, processing time. Also, it becomes difficult to train for high-dimensional medical datasets and extracting complex feature representations. Therefore, In this proposal, Image classification through a Scalable and Resource efficient QCNN is proposed which integrates classical CNN with quantum computing. This review examines the current state of medical image classification for tumor detection, analyzes the transition from classical CNNs to quantum-based architectures, and explores the potential of QCNNs in revolutionizing medical diagnostics. This research includes QCNN architecture which will include selective feature encoding using encoding technique such as Z Feature map and quantum circuits for convolutional layers and pooling layers to handle multi-modal medical data (e.g., MRI and CT scans). We aim to achieve superior accuracy with reduced parameters over classical CNNs. This work advances quantum ML toward practical deployment in resource-limited healthcare settings.
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.