Ahmad Ashraf, Abdul Halim and Andrew, Allan Melvin and Wan Azani, Mustafa and Mohd Najib, Mohd Yasin and Muzammil R.I., Mat Jusoh and Veeraperumal, Vijayasarveswari and Mohd Amiruddin, Abd Rahman and Norshuhani, Zamin and Mary, Mervin Retnadhas and Khatun, Sabira (2022) Optimized intelligent classifier for early breast cancer detection using ultra-wide band transceiver. Diagnostics, 12 (2870). pp. 1-19. ISSN 2075-4418. (Published)
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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.
| Item Type: | Article |
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| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | Breast cancer; Feature engineering; Feature selection; Machine learning; Multi-stage; Prediction; Supervised learning |
| Subjects: | T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Faculty/Division: | Faculty of Electrical and Electronic Engineering Technology |
| Depositing User: | Mr Muhamad Firdaus Janih@Jaini |
| Date Deposited: | 30 Apr 2024 06:31 |
| Last Modified: | 30 Apr 2024 06:31 |
| URI: | http://umpir.ump.edu.my/id/eprint/40598 |
| Download Statistic: | View Download Statistics |
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