Abstract
Breast cancer is the most common cancer diagnosed in women and the leading cause of cancer-related deaths among women worldwide. The death rate is high because of the lack of early signs. Due to the absence of a cure, immediate treatment is necessary to remove the cancerous cells and prolong life. For early breast cancer detection, it is crucial to propose a robust intelligent classifier with statistical feature analysis that considers parameter existence, size, and location. This paper proposes a novel Multi-Stage Feature Selection with Binary Particle Swarm Optimization (MSFS–BPSO) using Ultra-Wideband (UWB). A collection of 39,000 data samples from non-tumor and with tumor sizes ranging from 2 to 7 mm was created using realistic tissue-like dielectric materials. Subsequently, the tumor models were inserted into the heterogeneous breast phantom. The breast phantom with tumors was imaged and represented in both time and frequency domains using the UWB signal. Consequently, the dataset was fed into the MSFS–BPSO framework and started with feature normalization before it was reduced using feature dimension reduction. Then, the feature selection (based on time/frequency domain) using seven different classifiers selected the frequency domain compared to the time domain and continued to perform feature extraction. Feature selection using Analysis of Variance (ANOVA) is able to distinguish between class-correlated data. Finally, the optimum feature subset was selected using a Probabilistic Neural Network (PNN) classifier with the Binary Particle Swarm Optimization (BPSO) method. The research findings found that the MSFS–BPSO method has increased classification accuracy up to 96.3% and given good dependability even when employing an enormous data sample.
Reference51 articles.
1. Prediction of Breast Cancer Using Supervised Machine Learning Techniques;Int. J. Innov. Technol. Explor. Eng.,2019
2. A review of various modalities in breast imaging: Technical aspects and clinical outcomes;Egypt. J. Radiol. Nucl. Med.,2020
3. Bonsu, A.B., and Ncama, B.P. (2019). Integration of breast cancer prevention and early detection into cancer palliative care model. PLoS ONE, 14.
4. Amdaouch, I., Saban, M., El Gueri, J., Chaari, M.Z., Alejos, A.V., Alzola, J.R., Muñoz, A.R., and Aghzout, O. (2022). A Novel Approach of a Low-Cost UWB Microwave Imaging System with High Resolution Based on SAR and a New Fast Reconstruction Algorithm for Early-Stage Breast Cancer Detection. J. Imaging, 8.
5. Breast cancer detection by leveraging machine learning;Korean Inst. Commun. Inf. Sci.,2020
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Breast Tumor Detection and Classification Using ABC-ELM Algorithm;2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT);2023-06-09