Investigating the Thresholding Effect and Fingerprint Transformation Using Cross-Correlation Similarity Matching
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Date
2025-01-12
Journal Title
Journal ISSN
Volume Title
Publisher
Faculty of Science Lafiya
Abstract
One of the leading diseases globally is cancer and breast cancer is not exempted. The objective of the WHO Global Breast Cancer Initiative (GBCI) is to reduce global breast cancer mortality by 2.5% per year, thereby averting 2.5 million breast cancer deaths globally between 2020 and 2040. The three pillars toward achieving these objectives are: health promotion for early detection; timely diagnosis; and comprehensive breast cancer management. In this study we propose an early and comprehensive detection technique in combating breast cancer diagnosis by combining the strength of both PSO (Particle Swarm Optimization) and BPSO (Binary Particle Swarm Optimization) to achieve optimal solution. The results obtained indicated the superiority of the Hybrid PSO-BPSO model in detection over an existing solution by achieving an accuracy of 98.82% on both the WBCD and WDBC datasets.
Description
This research proposes a hybrid diagnostic model combining Particle Swarm Optimization (PSO) and Binary Particle Swarm Optimization (BPSO) to enhance early and accurate detection of breast cancer, aligning with the World Health Organization's Global Breast Cancer Initiative (GBCI) goal of reducing mortality by 2.5% annually. By integrating both continuous and binary optimization techniques, the study aims to improve the precision of breast cancer diagnosis, which is critical for timely treatment and management. The hybrid PSO-BPSO model demonstrated superior performance, achieving a high accuracy of 98.82% on both the WBCD and WDBC datasets, outperforming existing detection methods.
Keywords
Breast cancer, algorithm, optimization, particle swarm optimization
Citation
Garuba O.R., Abdullahi, I.M., Dogo, E.M., & Maliki, D. (2025). Investigating the Thresholding Effect and Fingerprint Transformation Using Cross-Correlation Similarity Matching. International Conference of the Faculty of Science, FULafia. https://lafiascijournals.org.ng/index.php/fscproceedings.2025. Pp 25-29.