Journal of Electronics Electromedical Engineering and Medical Informatics

A Quantum Convolutional Neural Network for Breast Cancer Classification using Boruta and GA-Based Feature Selection with Quantum Feature Maps

Veeranjaneyulu Pagadala, B. Venkatesh, Sindhu Boinapalli, Ramya Krishna Dhulipalla, S Annapoorna

Published Jul 13, 2025• 6 citations

Abstract

Accurate and computationally efficient classification systems are essential for the early detection of breast cancer, particularly when dealing with complex and high-dimensional medical datasets. Traditional machine learning models often face limitations in capturing intricate nonlinear relationships inherent in such data, potentially compromising diagnostic performance. In this study, we introduce QBG-QCNN, a Quantum-enhanced framework named Boruta-GA optimized Quantum Convolutional Neural Network, designed for breast cancer classification. The model is trained on the Breast Cancer Wisconsin (Diagnostic) Dataset, which contains 30 numerical features extracted from fine needle aspiration (FNA) images of breast tissue samples. To reduce dimensionality while preserving critical diagnostic information, a hybrid Boruta-GA feature selection strategy is applied to extract key features such as radius_mean, texture_mean, area_mean, and concavity_mean. These selected features are then encoded into a 4-qubit quantum circuit using advanced quantum feature maps ZZFeatureMap, RealAmplitudes, and EfficientSU2, eliminating the need for manual feature engineering. The encoded quantum data is processed through a QCNN that incorporates quantum convolution, pooling, and parameterized ansatz layers, leveraging quantum entanglement and parallelism for more efficient learning. Implemented using PennyLane and IBM Qiskit, and optimized with the COBYLA, the model achieves outstanding performance: 94.3% accuracy, 95.2% precision, 94.6% recall, and a 93.0% F1-score. These results significantly outperform those of classical CNNs, standard QNNs, and other hybrid models. In conclusion, QBG-QCNN demonstrates that quantum machine learning, when integrated with intelligent feature selection, offers a powerful, scalable, and interpretable solution for early-stage breast cancer diagnosis. Future research will extend this framework to multi-modal datasets and real-device deployment on real quantum devices under noise constraints.

DOI: 10.35882/jeeemi.v7i3.792

Related papers

QUANTUM NEURAL NETWORK FOR BREAST CANCER DETECTION

KANUMURI PUJITHA, CHELIKANI YASASWINI, JAKKA ROSHINI +2 more

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.

International journal of engineering science and advanced technology.Apr 1, 20260 citations

Leveraging Quantum Convolutional Neural Networks for Advanced Cancer Detection

Gherbi Badereddine, Hani Abdeljalil, Safia Djemame

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.

International Conference on Networking and Advanced Systems (ICNAS)Oct 29, 20251 citation

Breast Cancer Detection with Quanvolutional Neural Networks

Nadine Matondo-Mvula, Khaled M. Elleithy

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.

EntropyJul 26, 202422 citations

Comparative Evaluation Of Classical And Quantum Machine Learning Models For Breast Cancer Diagnosis And Sustainable Healthcare

Matam Santoshi Kumari, M. Sushanth Babu, H. Y. Shrivalli +1 more

Breast cancer is rated as one of the top leading diagnosed and deadliest cancers affecting women worldwide and causing high mortality rate among cancers. With the data-driven applications, hybrid quantum-classical machine learning models have evolved as significant alternatives for handling complex feature spaces with the growing interest in quantum computing. This paper presents a systematic comparative study of classical and quantum machine learning approaches for breast cancer classification using the Wisconsin Breast Cancer Dataset. In this architecture we have preprocessed classical feature vectors using standard normalized techniques and parallelly quantum states are mapped through data embedding techniques which relies on parameterized rotational gates. In this work we have implemented all quantum models using the Pennylane library, resulting in hybrid quantum–classical optimization on near-term quantum simulators. The comparison results in multiple classical and quantum models. In classical we have Support Vector Machine (SVM), K-Nearest Neighbors (KNN) and Convolutional Neural Networks (CNN), which are compared with the quantum-based models such as the Variational Quantum Classifier (VQC), Quantum Support Vector Machine (QSVM) and Quantum Convolutional Neural Network (QCNN). In QSVM, we have performed dimensionality reduction using Principal Component Analysis (PCA) before to quantum embedding, and quantum-generated features are used to train a classical linear SVM. The evaluation metrics results in accuracy, precision, recall, F1-score, ROC curves and confusion matrices. The classical models achieves the accuracy in the range of 94%-96%, whereas competitive performance of quantum models demonstrate depending on the embedding strategy and circuit configuration. In the implemented quantum models, the variational quantum classifier achieved the highest classification accuracy of 96.06%, surpassing other evaluated models. These results demonstrate quantum embedding combined with variational quantum circuits can effectively capture discriminative patterns in medical datasets, indicating the implementation of quantum machine learning models for breast cancer diagnosis with the present quantum simulating platforms.

International Journal of Environmental SciencesJan 6, 20260 citations

A hybrid QSVM-QNN approach for breast cancer diagnosis using quantum feature maps

Vikesh Yadav, Savita Kumari Sheoran, Rakesh Kumar Sheoran

Quantum assisted machine learning (QML) have the potential to outperform the classical Machine Learning models by utilizing the quantum feature maps embeddings and variational circuits. In this study, a Hybrid Quantum Support Vector Machine (QSVM) and Quantum Neural Network (QNN) is proposed for supervised classification applied to the Breast Cancer Wisconsin Dataset. The QSVM component employs fidelity quantum kernels with three distinct feature maps—Z, ZZ and Pauli—to transform classical data into quantum states. The QNN is implemented using a variational quantum classifier based on the EstimatorQNN, which enables quantum-trainable parameters for enhanced feature extraction. The proposed hybrid methodology is evaluated using seven principal features, and experiments are conducted on IBM’s state vector Qiskit Aer local simulator. Performance is assessed based on accuracy, recall, precision, F1-score, and confusion matrix. Additionally, statistical significance is evaluated using paired t test and F test to compare the performance of different quantum feature maps and to validate improvements made in the Hybrid Model. The results demonstrate that the hybrid QSVM/QNN approach improves classification performance not only in terms of accuracy by achieving above 90% accuracy but also by reducing computational cost. This work contributes to the advancement of quantum enhanced machine learning (ML) algorithms by demonstrating the hybrid quantum-classical approach in structured data classification.

PeerJ Computer ScienceMar 3, 20263 citations

Comparing Classical and Quantum Machine Learning Models for Breast Cancer Classification on the WDBC Dataset

Jovana Gluhovic

Breast cancer is a major health concern, and early detection can make a great difference in treatment and survival rates for breast cancer patients. Machine learning methods have noticeably improved prediction accuracy on high-dimensional medical datasets and have become widely used tools in medical diagnostics. The transition to a quantum computational framework has opened new directions in machine learning research. By using qubits instead of classical bits, quantum models may provide new ways to represent and process complex medical data. In medical diagnostics, this has led to a growing interest in whether quantum approaches can improve classification performance more effectively than classical methods. The purpose of this paper is to compare classical and quantum machine learning models on the task of breast cancer classification and to determine whether quantum models can achieve higher accuracy and faster prediction on a selected dataset. Alongside the main simulator-based experiment, a smaller experiment was performed on a real IBM quantum computer to demonstrate the practical execution of the same task under current hardware constraints. In the practical part of the study, the Wisconsin Diagnostic Breast Cancer (WDBC) dataset was used. This dataset consists of 569 samples with 30 numerical features extracted from digitized fine needle aspirate images and provides a reliable basis for evaluating model performance. A comparative analysis was conducted between classical and quantum machine learning models, including a support vector machine (SVM), an artificial neural network (ANN), a quantum support vector machine (QSVM), and a hybrid quantum-classical neural network (QNN). In the simulator-based experiment, both SVM and ANN achieved an accuracy of 0.956 with an F1-score of 0.965 on the test set, while QSVM reached an accuracy of 0.807 with an F1-score of 0.866 and the Hybrid QNN achieved 0.623 accuracy with an F1-score of 0.677. These quantum and hybrid models also required substantially longer training times than the classical baselines. A small hardware experiment on an IBM quantum device further illustrates both the practical feasibility and the current limitations of executing this classification task on noisy intermediate-scale quantum hardware.

American Journal of Computer Science and TechnologyAug 6, 20260 citations