An AI-Driven Diagnostic Decision Support System Using EP-Optimized RBF Neural Networks for Breast Cancer Detection
Corresponding author: S. G. Shaila
Abstract
Breast cancer remains one of the most prevalent diseases that affects women worldwide. Early diagnosis is crucial, as it significantly reduces the risk of cancer spreading to lymph nodes or distant organs. This study presents an approach that integrates Artificial Intelligence (AI) to develop an effective diagnostic decision support system based on features extracted from biopsy tissue samples. A hybrid model of Evolutionary Programming (EP) with Radial Basis Function (RBF) Neural Network (NN) was developed to enhance robustness and classification accuracy. Although NNs are capable of learning complex patterns from previously unseen data, traditional gradient-based learning methods are prone to getting trapped in local minima. To address this limitation, an Evolutionary Programming Adaptive Radial Basis Function (EPARBF) is incorporated to provide adaptive learning by optimizing kernel parameters and output layer weights of the RBF neural network using a Differential Evolution (DE)-based heuristic approach. The proposed approach uses a fully adaptive RBF network on Fine-Needle Aspiration (FNA) biopsy data and classifies breast cancer cases as benign or malignant. The model achieved high sensitivity and specificity across both training and test datasets, significantly outperforming static RBF models and Gradient-based adaptive networks, demonstrating strong generalization and classification capability.
Keywords:
biopsy, breast cancer, benign, malignant, neural network, evolutionary programming, radial basis functionDownloads
References
"Cancer," World Health Organization. https://www.who.int/health-topics/cancer.
H. Hussein et al., "Supplemental Breast Cancer Screening in Women with Dense Breasts and Negative Mammography: A Systematic Review and Meta-Analysis," Radiology, vol. 306, no. 3, Mar. 2023, Art. no. e221785. DOI: https://doi.org/10.1148/radiol.221785
E. D. Pisano et al., "Fine-Needle Aspiration Biopsy of Nonpalpable Breast Lesions in a Multicenter Clinical Trial: Results from the Radiologic Diagnostic Oncology Group V," Radiology, vol. 219, no. 3, pp. 785–792, Jun. 2001. DOI: https://doi.org/10.1148/radiology.219.3.r01jn28785
A. Osareh and B. Shadgar, "Machine learning techniques to diagnose breast cancer," in 2010 5th International Symposium on Health Informatics and Bioinformatics, Ankara, Turkey, 2010, pp. 114–120. DOI: https://doi.org/10.1109/HIBIT.2010.5478895
S. Silva, O. Anunciação, and M. Lotz, "A Comparison of Machine Learning Methods for the Prediction of Breast Cancer," in Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, vol. 6623, C. Pizzuti, M. D. Ritchie, and M. Giacobini, Eds. Springer Berlin Heidelberg, 2011, pp. 159–170. DOI: https://doi.org/10.1007/978-3-642-20389-3_17
R. Ramos-Pollán et al., "Discovering Mammography-based Machine Learning Classifiers for Breast Cancer Diagnosis," Journal of Medical Systems, vol. 36, no. 4, pp. 2259–2269, Aug. 2012. DOI: https://doi.org/10.1007/s10916-011-9693-2
S. Mani et al., "Machine learning for predicting the response of breast cancer to neoadjuvant chemotherapy," Journal of the American Medical Informatics Association, vol. 20, no. 4, pp. 688–695, Jul. 2013. DOI: https://doi.org/10.1136/amiajnl-2012-001332
J. Fombellida, S. Torres-Alegre, J. A. Piñuela-Izquierdo, and D. Andina, "Artificial Metaplasticity for Deep Learning: Application to WBCD Breast Cancer Database Classification," in Bioinspired Computation in Artificial Systems, vol. 9108, J. M. Ferrández Vicente, J. R. Álvarez-Sánchez, F. De La Paz López, Fco. J. Toledo-Moreo, and H. Adeli, Eds. Springer International Publishing, 2015, pp. 399–408. DOI: https://doi.org/10.1007/978-3-319-18833-1_42
H. Asri, H. Mousannif, H. A. Moatassime, and T. Noel, "Using Machine Learning Algorithms for Breast Cancer Risk Prediction and Diagnosis," Procedia Computer Science, vol. 83, pp. 1064–1069, 2016. DOI: https://doi.org/10.1016/j.procs.2016.04.224
T. Saba, "Recent advancement in cancer detection using machine learning: Systematic survey of decades, comparisons and challenges," Journal of Infection and Public Health, vol. 13, no. 9, pp. 1274–1289, Sep. 2020. DOI: https://doi.org/10.1016/j.jiph.2020.06.033
S. A. Mohammed, S. Darrab, S. A. Noaman, and G. Saake, "Analysis of Breast Cancer Detection Using Different Machine Learning Techniques," in Data Mining and Big Data, vol. 1234, Y. Tan, Y. Shi, and M. Tuba, Eds. Springer Singapore, 2020, pp. 108–117. DOI: https://doi.org/10.1007/978-981-15-7205-0_10
P. H. Abreu, M. S. Santos, M. H. Abreu, B. Andrade, and D. C. Silva, "Predicting Breast Cancer Recurrence Using Machine Learning Techniques: A Systematic Review," ACM Computing Surveys, vol. 49, no. 3, Jul. 2016, Art. no. 52. DOI: https://doi.org/10.1145/2988544
B. Bektaş and S. Babur, "Machine learning based performance development for diagnosis of breast cancer," in 2016 Medical Technologies National Congress (TIPTEKNO), Antalya, Turkey, Oct. 2016, pp. 1–4. DOI: https://doi.org/10.1109/TIPTEKNO.2016.7863129
N. Kolay and P. Erdoğmuş, "The classification of breast cancer with Machine Learning Techniques," in 2016 Electric Electronics, Computer Science, Biomedical Engineerings’ Meeting (EBBT), Istanbul, Turkey, Apr. 2016, pp. 1–4. DOI: https://doi.org/10.1109/EBBT.2016.7483683
S. Kim, "Weighted K-means support vector machine for cancer prediction," SpringerPlus, vol. 5, no. 1, Dec. 2016, Art. no. 1162. DOI: https://doi.org/10.1186/s40064-016-2677-4
M. Montazeri, M. Montazeri, M. Montazeri, and A. Beigzadeh, "Machine learning models in breast cancer survival prediction," Technology and Health Care, vol. 24, no. 1, pp. 31–42, Jan. 2016. DOI: https://doi.org/10.3233/THC-151071
M. W. Huang, C. W. Chen, W. C. Lin, S. W. Ke, and C. F. Tsai, "SVM and SVM Ensembles in Breast Cancer Prediction," PLOS ONE, vol. 12, no. 1, 2017, Art. no. e0161501. DOI: https://doi.org/10.1371/journal.pone.0161501
L. Abdel-Ilah and H. Šahinbegović, "Using machine learning tool in classification of breast cancer," in CMBEBIH 2017, 2017, pp. 3–8. DOI: https://doi.org/10.1007/978-981-10-4166-2_1
J. P. Leone et al., "Factors associated with late risks of breast cancer-specific mortality in the SEER registry," Breast Cancer Research and Treatment, vol. 189, no. 1, pp. 203–212, Aug. 2021. DOI: https://doi.org/10.1007/s10549-021-06233-4
J. J. J. Gommers et al., "The Impact of Preoperative Breast MRI on Surgical Margin Status in Breast Cancer Patients Recalled at Biennial Screening Mammography: An Observational Cohort Study," Annals of Surgical Oncology, vol. 28, no. S3, pp. 432–432, Dec. 2021. DOI: https://doi.org/10.1245/s10434-021-10001-5
A. A. Hekal, A. Elnakib, and H. E. D. Moustafa, "Automated early breast cancer detection and classification system," Signal, Image and Video Processing, vol. 15, no. 7, pp. 1497–1505, Oct. 2021. DOI: https://doi.org/10.1007/s11760-021-01882-w
R. Karthiga and K. Narasimhan, "Medical imaging technique using curvelet transform and machine learning for the automated diagnosis of breast cancer from thermal image," Pattern Analysis and Applications, vol. 24, no. 3, pp. 981–991, Aug. 2021. DOI: https://doi.org/10.1007/s10044-021-00963-3
I. Allajbeu et al., "Automated Breast Ultrasound: Technical Aspects, Impact on Breast Screening, and Future Perspectives," Current Breast Cancer Reports, vol. 13, no. 3, pp. 141–150, Sep. 2021. DOI: https://doi.org/10.1007/s12609-021-00423-1
O. M. William Wolberg, "Breast Cancer Wisconsin (Diagnostic)." UCI Machine Learning Repository, 1993.
S. Altintas and W. Tjalma, "Pregnancy-Associated Breast Cancer (PABC) and Fertility Issues in Young Women After Breast Cancer," in Breast Cancer Essentials, M. Rezai, M. A. Kocdor, and N. Z. Canturk, Eds. Springer International Publishing, 2021, pp. 657–665. DOI: https://doi.org/10.1007/978-3-030-73147-2_58
A. K. Kattepur and K. S. Gopinath, "Management of Hereditary Breast Cancer: An Overview," in Breast Cancer, S. C. Sharma, A. Mazumdar, and R. Kaushik, Eds. Springer Nature Singapore, 2022, pp. 353–397. DOI: https://doi.org/10.1007/978-981-16-4546-4_18
P. Hays, "Great Strides in Precision Medicine: Personalized Oncology, Immunotherapies, and Molecular Diagnostics," in Advancing Healthcare Through Personalized Medicine, Springer International Publishing, 2021, pp. 141–417. DOI: https://doi.org/10.1007/978-3-030-80100-7_5
V. Inamdar, S. G. Shaila, and M. K. Singh, "FNAB-Based Prediction of Breast Cancer Category Using Evolutionary Programming Neural Ensemble," in Computational Vision and Bio-Inspired Computing, 2021, pp. 653–663. DOI: https://doi.org/10.1007/978-981-33-6862-0_51
M. Sreevani and R. Latha, "A Deep Learning with Metaheuristic Optimization-Driven Breast Cancer Segmentation and Classification Model using Mammogram Imaging," Engineering, Technology & Applied Science Research, vol. 15, no. 1, pp. 20342–20347, Feb. 2025. DOI: https://doi.org/10.48084/etasr.9406
V. R. Gurudas, S. G. Shaila, and A. Vadivel, "Breast Cancer Detection and Classification from Mammogram Images Using Multi-model Shape Features," SN Computer Science, vol. 3, no. 5, Jul. 2022, Art. no. 404. DOI: https://doi.org/10.1007/s42979-022-01290-y
V. R. Gurudas, S. G. Shaila, and A. Vadivel, "Morphological and Textural Data Fusion for Breast Cancer Classification Based on Inter and Intra group Variances," International Journal of Intelligent Engineering and Systems, vol. 17, no. 3, pp. 682–695, Jun. 2024. DOI: https://doi.org/10.22266/ijies2024.0630.53
"Breast Cancer Wisconsin (Diagnostic) Data Set." Kaggle, [Online]. Available: https://www.kaggle.com/datasets/uciml/breast-cancer-wisconsin-data.
S. P. Rana et al., "Radial Basis Function for Breast Lesion Detection from MammoWave Clinical Data," Diagnostics, vol. 11, no. 10, Oct. 2021, Art. no. 1930. DOI: https://doi.org/10.3390/diagnostics11101930
A. Ponraj and A. Canessane, "Radial Basis Function Networks and Contrast-Limited Adaptive Histogram Equalization Filter Based Early-Stage Breast Cancer Detection Techniques," Journal of Computer Science, vol. 19, no. 6, pp. 760–774, Jun. 2023. DOI: https://doi.org/10.3844/jcssp.2023.760.774
H. A. Essa, E. Ismaiel, and M. F. A. Hinnawi, "Feature-based detection of breast cancer using convolutional neural network and feature engineering," Scientific Reports, vol. 14, no. 1, Sep. 2024, Art. no. 22215. DOI: https://doi.org/10.1038/s41598-024-73083-7
C. Beltran-Perez, H. L. Wei, and A. Rubio-Solis, "Generalized Multiscale RBF Networks and the DCT for Breast Cancer Detection," International Journal of Automation and Computing, vol. 17, no. 1, pp. 55–70, Feb. 2020. DOI: https://doi.org/10.1007/s11633-019-1210-y
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