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September 10, 2025Open Access

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

Key Points

The quantum convolutional neural network achieved 94.3% accuracy in breast cancer classification, significantly outpacing traditional models.
Precision was recorded at 95.2% and recall at 94.6%, showcasing the method's effectiveness in detecting cancerous samples.
Feature selection through Boruta-GA identified key numerical features from fine needle aspiration images to enhance model performance.
Leveraging quantum feature maps allows for reduced dimensionality, improving efficiency for high-dimensional medical datasets.

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 radiusₘean, textureₘean, areaₘean, and concavityₘean. 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.

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