Breast cancer detection via wavelet energy and feed-forward neural network trained by genetic algorithm




wavelet energy, breast cancer, genetic algorithm


Enhancing the precision of breast cancer detection is the primary objective of this investigation, given its status as the most prevalent cancer among women worldwide. Timely identification of breast cancer can significantly improve the likelihood of successful diagnosis. To achieve this, we propose a innovative way that combines wavelet energy and a feedforward neural network. Our method employs the genetic algorithm and undergoes 20 iterations of 10-fold cross-validation for robustness. Via utilizing wavelet energy as a feature extractor and a feedforward neural network as the classifier, our method outperforms three alternative algorithms.


Download data is not yet available.


Nandi, D., S. Parida, and D. Sharma, The gut microbiota in breast cancer development and treatment: The good, the bad, and the useful! Gut Microbes, 2023. 15(1).

Silva-Aravena, F., et al., A hybrid algorithm of ML and XAI to prevent breast cancer: a strategy to support decision making. Cancers, 2023. 15(9): p. 2443.

Aslan, A.A. and S. Gultekin, Diagnostic performance of Kaiser score in patients with newly diagnosed breast cancer: Factors associated with false-negative results. European Journal of Radiology, 2023. 164.

Hudson-Phillips, S., et al., Paget's disease of the breast: diagnosis and management. British Journal of Hospital Medicine, 2023. 84(1): p. 1-8.

Ojo, A.S., et al., Synchronous Bilateral Breast Cancer With Discordant Receptor Status: Treating One Patient but Two Diseases. World Journal of Oncology, 2023. 14(3): p. 224.

Tabar, L., et al., Multifocal and diffusely infiltrating breast cancers are highly fatal subgroups needing further improvement in diagnostic and therapeutic strategies. European Journal of Radiology, 2023. 164.

Djojosaputro, M., E.S.D. Pohan, and M.Y.U. Putri, Use of hormone replacement therapy for increasing breast cancer incidence rates. International Journal of Medical and Health Research, 2023. 9(3): p. 17-23.

Stordal, B., Breastfeeding reduces the risk of breast cancer: A call for action in high‐income countries with low rates of breastfeeding. Cancer Medicine, 2023. 12(4): p. 4616-4625.

Guttery, D.S., Improved Breast Cancer Classification Through Combining Graph Convolutional Network and Convolutional Neural Network. Information Processing and Management, 2021. 58.

Pignatelli, P., et al., Reactive Oxygen Species Produced by 5-Aminolevulinic Acid Photodynamic Therapy in the Treatment of Cancer. International Journal of Molecular Sciences, 2023. 24(10): p. 8964.

Resteghini, C., et al., The SINTART 1 study. A phase II non-randomised controlled trial of induction chemotherapy, surgery, photon-, proton- and carbon ion-based radiotherapy integration in patients with locally advanced resectable sinonasal tumours. European Journal of Cancer, 2023. 187: p. 185-194.

Seki, M., et al., Relationship between histopathological therapeutic effect and prognosis in oral cancer patients after preoperative S-1 chemotherapy followed by surgery. Clinical Oral Investigations, 2023.

Ağralı, M., et al., DeepChestNet: Artificial intelligence approach for COVID‐19 detection on computed tomography images. International Journal of Imaging Systems and Technology, 2023. 33(3): p. 776-788.

Ibad, H.A., et al., Computed tomography: state-of-The-art advancements in musculoskeletal imaging. Investigative radiology, 2023. 58(1): p. 99-110.

Igarashi, T., H. Kim, and P.Z. Sun, Detection of tissue pH with quantitative chemical exchange saturation transfer magnetic resonance imaging. NMR in Biomedicine, 2023. 36(6): p. e4711.

Jiang, S., et al., Applications of chemical exchange saturation transfer magnetic resonance imaging in identifying genetic markers in gliomas. NMR in Biomedicine, 2023. 36(6): p. e4731.

Dimitrakopoulou-Strauss, A., et al., Positron Emission Tomography-Based Immunoimaging for Cancer Patient Stratification: Toward a More Holistic Approach. Cancer Biotherapy & Radiopharmaceuticals, 2023. 38(4): p. 225-231.

Zhou, Q., et al., WVALE: Weak Variational Autoencoder for Localisation and Enhancement of COVID-19 Lung Infections. Computer Methods and Programs in Biomedicine, 2022. 221: p. 106883.

Cahyo, L.M. and S.D. Astuti, Early Detection of Health Problems through Artificial Intelligence (Ai) Technology in Hospital Information Management: A Literature Review Study. Journal of Medical and Health Studies, 2023. 4(3): p. 37-42.

Farah, L., et al., Assessment of Performance, Interpretability, and Explainability in Artificial Intelligence–Based Health Technologies: What Healthcare Stakeholders Need to Know. Mayo Clinic Proceedings: Digital Health, 2023. 1(2): p. 120-138.

Yan, F., et al., Automated breast cancer detection in mammography using ensemble classifier and feature weighting algorithms. Expert Systems with Applications, 2023. 227: p. 120282.

Avcı, H. and J. Karakaya, A Novel Medical Image Enhancement Algorithm for Breast Cancer Detection on Mammography Images Using Machine Learning. Diagnostics, 2023. 13(3): p. 348.

Nguyen, E., Breast cancer detection via Hu moment invariant and feedforward neural network. AIP Conference Proceedings, 2018. 1954(1).

Rao, R.V., Abnormal Breast Detection in Mammogram Images by Feed-forward Neural Network trained by Jaya Algorithm. Fundamenta Informaticae, 2017. 151: p. 191-211.

Pan, C., Abnormal breast identification by nine-layer convolutional neural network with parametric rectified linear unit and rank-based stochastic pooling. Journal of Computational Science, 2018. 27: p. 57-68.

Wang, J., et al., COVID-19 Detection Based on 6-Layered Explainable Customized Convolutional Neural Network. CMES-Computer Modeling in Engineering & Sciences, 2023. 136(3).

Aljameel, S.S., et al., Predictive Artificial Intelligence Model for Detecting Dental Age Using Panoramic Radiograph Images. Big Data and Cognitive Computing, 2023. 7(1): p. 8.

The mini-MIAS database of mammograms. 2016; Available from:

Mushari, N.A., et al., Exploring the Utility of Cardiovascular Magnetic Resonance Radiomic Feature Extraction for Evaluation of Cardiac Sarcoidosis. Diagnostics, 2023. 13(11).

Subahi, A. and M. Almasre, IoT Traffic Analyzer Tool with Automated and Holistic Feature Extraction Capability. Sensors, 2023. 23(11).

Zhang, Y., Pathological brain detection in MRI scanning via Hu moment invariants and machine learning. Journal of Experimental & Theoretical Artificial Intelligence, 2017. 29(2): p. 299-312.

Kumar, Y., M.L. Dewal, and R.S. Anand, Relative wavelet energy and wavelet entropy based epileptic brain signals classification. Biomedical Engineering Letters, 2012. 2(3): p. 147-157.

Chacko, B.P., et al., Handwritten character recognition using wavelet energy and extreme learning machine. International Journal of Machine Learning and Cybernetics, 2012. 3(2): p. 149-161.

Wong, Y.J., S.K. Arumugasamy, and J. Jewaratnam, Performance comparison of feedforward neural network training algorithms in modeling for synthesis of polycaprolactone via biopolymerization. Clean Technologies and Environmental Policy, 2018. 20(9): p. 1971-1986.

Dong, C., B. Jin, and D. Li, Predicting the heating value of MSW with a feed forward neural network. Waste Management, 2003. 23(2): p. 103-106.

Shen, M.K., et al., Automatic identification of schizophrenia based on EEG signals using dynamic functional connectivity analysis and 3D convolutional neural network. Computers in Biology and Medicine, 2023. 160.

Hejduk, P., et al., Automatic and standardized quality assurance of digital mammography and tomosynthesis with deep convolutional neural networks. Insights into Imaging, 2023. 14(1).

Muhammad, K., Image based fruit category classification by 13-layer deep convolutional neural network and data augmentation. Multimedia Tools and Applications, 2019. 78(3): p. 3613-3632.

Zhang, Y., Pathological brain detection in MRI scanning by wavelet packet Tsallis entropy and fuzzy support vector machine. SpringerPlus, 2015. 4(1).

Neuendorf, L., et al., Convolutional Neural Network (CNN)-Based Measurement of Properties in Liquid-Liquid Systems. Processes, 2023. 11(5).

Kai, C., et al., Automated Estimation of Mammary Gland Content Ratio Using Regression Deep Convolutional Neural Network and the Effectiveness in Clinical Practice as Explainable Artificial Intelligence. Cancers, 2023. 15(10).

Malik, A.K., M.A. Ganaie, and M. Tanveer. Graph embedded intuitionistic fuzzy weighted random vector functional link network. in IEEE Symposium Series on Computational Intelligence (IEEE SSCI). 2022. Singapore, SINGAPORE.

Rosalsky, A. and L.V. Thanh, Optimal moment conditions for complete convergence for maximal normed weighted sums from arrays of rowwise independent random elements in Banach spaces. Revista De La Real Academia De Ciencias Exactas Fisicas Y Naturales Serie a-Matematicas, 2023. 117(3).

Wang, S.-H., Unilateral sensorineural hearing loss identification based on double-density dual-tree complex wavelet transform and multinomial logistic regression. Integrated Computer-Aided Engineering, 2019. 26: p. 411-426.

Zhang, Y.-D., High performance multiple sclerosis classification by data augmentation and AlexNet transfer learning model. Journal of Medical Imaging and Health Informatics, 2019. 9(9): p. 2012-2021.

Mbamba, C.K. and D.J. Batstone, Optimization of deep learning models for forecasting performance in the water industry using genetic algorithms. Computers & Chemical Engineering, 2023. 175.

Oladipo, S. and Y.X. Sun, Enhanced adaptive neuro-fuzzy inference system using genetic algorithm: a case study in predicting electricity consumption. SN Applied Sciences, 2023. 5(7).

Mohammadkhani, L.G., J. Ghorbani, and M. Kompany-Zareh, Resampling for estimation of parameters uncertainty in genetic algorithm based model fitting. Microchemical Journal, 2023. 189.

Giouvanakis, M., C. Sevastiadis, and G. Papanikolaou, Active Low-Frequency Noise Control Implementing Genetic Algorithm on Mode Coupling of a Compound Source. Applied Sciences-Basel, 2023. 13(11).

Sebek, M., et al., A Genetic Algorithm for Universal Optimization of Ultrasensitive Surface Plasmon Resonance Sensors with 2D Materials. Acs Omega, 2023. 8(23): p. 20792-20800.

Nishat, F., et al., Cross-cultural adaptation and validation of the Bangla version of the Psoriasis Disability Index. Journal of Public Health Research, 2023. 12(2).

Zhang, Y., Deep learning in food category recognition. Information Fusion, 2023. 98: p. 101859.

Andreu-March, M., et al., Cross-cultural adaptation and validation of the Recognizing And Addressing Limited Pharmaceutical Literacy (RALPH) interview guide in community pharmacies. Research in Social & Administrative Pharmacy, 2023. 19(6): p. 882-888.

Jiang, X., Chinese Sign Language Fingerspelling Recognition via Six-Layer Convolutional Neural Network with Leaky Rectified Linear Units for Therapy and Rehabilitation. Journal of Medical Imaging and Health Informatics, 2019. 9(9): p. 2031-2038.

Liu, F. and M. Brown, Breast Cancer Recognition by Support Vector Machine Combined with Daubechies Wavelet Transform and Principal Component Analysis. Lecture Notes in Computational Vision and Biomechanics, 2019. 30: p. 1921-1930.

Guo, Z.-W. Breast cancer detection via wavelet energy and support vector machine. in 27th IEEE International Conference on Robot and Human Interactive Communication (ROMAN). 2018. Nanjing, China: IEEE.




How to Cite

J. Wang, “Breast cancer detection via wavelet energy and feed-forward neural network trained by genetic algorithm”, EAI Endorsed Trans AI Robotics, vol. 2, Sep. 2023.