A Quantum Convolutional Neural Network for Breast Cancer Classification using Boruta and GA-Based Feature Selection with Quantum Feature Maps
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.
Downloads
References
Siegel RL, Kratzer TB, Giaquinto AN, Sung H, Jemal A. Cancer statistics, 2025. CA Cancer J Clin. 2025 Jan-Feb;75(1):10-45. doi: 10.3322/caac.21871.
. Giaquinto AN, Sung H, Newman LA, et al. Breast cancer statistics 2024. CA Cancer J Clin. 2024; 1-19. doi:10.3322/caac.21863
. Waks AG, Winer EP. Breast Cancer Treatment: A Review. JAMA. 2019 Jan 22;321(3):288-300. doi: 10.1001/jama.2018.19323.
. Prajapati, J.B., Paliwal, H., Prajapati, B.G., Saikia, S., Pandey, R. (2023). Quantum Machine Learning in Prediction of Breast Cancer. In: Pandey, R., Srivastava, N., Singh, N.K., Tyagi, K. (eds) Quantum Computing: A Shift from Bits to Qubits. Studies in Computational Intelligence, vol 1085. Springer, Singapore. https://doi.org/10.1007/978-981-19-9530-9_19
. Cerezo, Marco, et al. "Challenges and opportunities in quantum machine learning." Nature computational science 2.9 (2022): 567-576.
. Shan, Zheng & Guo, Jiayu & Ding, Xiaodong & Zhou, Xin & Wang, Junchao & Lian, Hang & Gao, Yufei & Zhao, Bo & Xu, Jinchen. (2022). Demonstration of Breast Cancer Detection Using QSVM on IBM Quantum Processors. 10.21203/rs.3.rs-1434074/v1.
. Gao, L., Lu, C., Guo, G., Zhang, X., & Lin, S. (2022). Quantum K-nearest neighbors classification algorithm based on Mahalanobis distance. Frontiers in Physics, 10, 1047466. https://doi.org/10.3389/fphy.2022.1047466
. Tarun Kumar, Dilip Kumar and Gurmohan Singh (2022), Performance Analysis of Quantum Classifier on Benchmarking Datasets. IJEER 10(2), 375-380. DOI: 10.37391/IJEER.100252.
. Schmitt, I. (2022). QLDT: A Decision Tree Based on Quantum Logic. In: Chiusano, S., et al. New Trends in Database and Information Systems. ADBIS 2022. Communications in Computer and Information Science, vol 1652. Springer, Cham. https://doi.org/10.1007/978-3-031-15743-1_28.
. Zhang, Y., & Ni, Q. (2020). Recent advances in quantum machine learning. Quantum Engineering, 2(1), e34. https://doi.org/10.1002/que2.34
. Saini, S., Khosla, P., Kaur, M. et al. Quantum Driven Machine Learning. Int J Theor Phys 59, 4013–4024 (2020). https://doi.org/10.1007/s10773-020-04656-1
. Hur, T., Kim, L. & Park, D.K. Quantum convolutional neural network for classical data classification. Quantum Mach. Intell. 4, 3 (2022). https://doi.org/10.1007/s42484-021-00061-x
. Öznacar, T., & Güler, T. (2025). Prediction of Early Diagnosis in Ovarian Cancer Patients Using Machine Learning Approaches with Boruta and Advanced Feature Selection. Life, 15(4), 594. https://doi.org/10.3390/life15040594
. R. Ibarrondo, G. Gatti and M. Sanz, "Quantum vs classical genetic algorithms: A numerical comparison shows faster convergence," 2022 IEEE Symposium Series on Computational Intelligence (SSCI), Singapore, Singapore, 2022, pp. 947-954, doi: 10.1109/SSCI51031.2022.10022159.
. Biamonte, J., Wittek, P., Pancotti, N. et al. Quantum machine learning. Nature 549, 195–202 (2017). https://doi.org/10.1038/nature23474.
. Ayoade, O., Rivas, P., Orduz, J., & Rafi, N. (2022). Satellite image classification using quantum machine learning. Artificial Intelligence in Earth Science, 337-355. https://doi.org/10.1016/B978-0-323-91737-7.00013-X
. Panchal, J. M. (2025). Quantum machine learning: Bridging quantum computing and artificial intelligence. KJCS: Kadi Journal of Computer Science, 3(1). https://doi.org/10.52783/kjcs.277
. Li, X., Jiang, H., Huang, S., & Tian, F. (2020). Robustness aware 2 bit quantization with real time performance for neural network (arXiv:2010.11271). https://doi.org/10.48550/arXiv.2010.11271.
. Sher, A., Trusov, A., Limonova, E., Nikolaev, D., & Arlazarov, V. V. (2022). Neuron-by-Neuron Quantization for Efficient Low-Bit QNN Training. Mathematics, 11(9), 2112. https://doi.org/10.3390/math11092112
. Mani, V., Saravanaselvan, A., & Arumugam, N. (2021). Performance comparison of CNN, QNN and BNN deep neural networks for real-time object detection using ZYNQ FPGA node. Microelectronics Journal, 119, 105319. https://doi.org/10.1016/j.mejo.2021.105319
. Alam, M., Kundu, S., Topaloglu, R. O., & Ghosh, S. (2021). Quantum classical hybrid machine learning for image classification (ICCAD Special Session Paper) (arXiv:2109.02862). https://doi.org/10.48550/arXiv.2109.02862
. Xiang, Q., Li, D., Hu, Z. et al. Quantum classical hybrid convolutional neural networks for breast cancer diagnosis. Sci Rep 14, 24699 (2024). https://doi.org/10.1038/s41598-024-74778-7
. Chen, S. Y., Wei, T. C., Zhang, C., Yu, H., & Yoo, S. (2021). Hybrid quantum classical graph convolutional network (arXiv:2101.06189). https://doi.org/10.48550/arXiv.2101.06189
. Huang, S.-Y., An, W.-J., Zhang, D.-S., & Zhou, N.-R. (2023). Image classification and adversarial robustness analysis based on hybrid quantum–classical convolutional neural network. Optics Communications, 533, 129287. https://doi.org/10.1016/j.optcom.2023.129287.
. Zhao, R., & Wang, S. (2021). A review of quantum neural networks: methods, models, dilemma (arXiv:2109.01840). https://doi.org/10.48550/arXiv.2109.01840
. Rahul Kumar Yadav, Pardeep Singh, Poonam Kashtriya, Diagnosis of Breast Cancer using Machine Learning Techniques -A Survey, Procedia Computer Science, Volume 218,2023, Pages 1434-1443, ISSN 1877- 0509, https://doi.org/10.1016/j.procs.2023.01.122
. Zeguendry, A.; Jarir, Z.; Quafafou, M. Quantum Machine Learning: A Review and Case Studies. Entropy 2023, 25, 287. https://doi.org/10.3390/e25020287
. Mishra, N. et al. (2021). Quantum Machine Learning: A Review and Current Status. In: Sharma, N., Chakrabarti, A., Balas, V.E., Martinovic, J. (eds) Data Management, Analytics and Innovation. Advances in Intelligent Systems and Computing, vol 1175. Springer, Singapore. https://doi.org/10.1007/978-981-15-5619-7_8
. Sahib, A. Y., Al Ali, M., & Al Ali, M. (2023). Investigation of early-stage breast cancer detection using quantum neural network. International Journal of Online and Biomedical Engineering (iJOE), 19(03). https://doi.org/10.3991/ijoe.v19i03.37573
. Afolayan, J.O., Adebiyi, M.O., Arowolo, M.O., Chakraborty, C., Adebiyi, A.A. (2022). Breast Cancer Detection Using Particle Swarm Optimization and Decision Tree Machine Learning Technique. In: Chakraborty, C., Khosravi, M.R. (eds) Intelligent Healthcare. Springer, Singapore. https://doi.org/10.1007/978-981-16-8150-9_4
. A. S. Elkorany, M. Marey, K. M. Almustafa and Z. F. Elsharkawy, "Breast Cancer Diagnosis Using Support Vector Machines Optimized by Whale Optimization and Dragonfly Algorithms," in IEEE Access, vol. 10, pp. 69688-69699, 2022, doi: 10.1109/ACCESS.2022.3186021.
. Mergin, A. A., & Premi, M. S. G. (2024). Convolutional neural networks (CNN) with quantum-behaved particle swarm optimization (QPSO)-based medical image fusion. International Journal of Image and Graphics, 24(05), 2340005. https://doi.org/10.1142/S0219467823400053
. Houssein, E. H., Abohashima, Z., Elhoseny, M., & Mohamed, W. M. (2021). An Efficient Binary Harris Hawks Optimization based on Quantum SVM for Cancer Classification Tasks. In IET Conference Proceedings 8 (2021), pp. 247-258. Institution of Engineering and technology, https://doi.org/10.1049/icp.2021.2680
. ZZFeatureMap. https://qiskit.org/documentation/stubs/qiskit.circuit.library.ZZFeatureMap.html
. RealAmplitudes, and EfficientSU2 https://docs.quantum.ibm.com/api/qiskit/0.24/
. Breast-cancer Data, https://www.kaggle.com/uciml/breast-cancer-wisconsin-data
. V. Bergholm et al., “PennyLane: Automatic differentiation of hybrid quantum-classical computations,” Nov. 2018, Accessed: Apr. 14, 2025. [Online]. Available: https://arxiv.org/abs/1811.04968v4
Copyright (c) 2025 Veeranjaneyulu Pagadala, Venkatesh B, Sindhu Boinapalli, Ramya Krishna Dhulipalla, S Annapoorna
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlikel 4.0 International (CC BY-SA 4.0) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
.png)
.png)
.png)
.png)
.png)
