School of Electrical Engineering and Technology (SEET)
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School of Electrical Engineering and Technology (SEET)
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Item A Joint Optimization Scheme for Enhanced Breast Cancer Diagnosis using Particle Swarm Optimization (PSO) and Binary Particle Swarm Optimization (BPSO)(International Conference of the Faculty of Science, 2025-01-14) Ahmed, Y.E., Abdullahi, I.M., Maliki, D., & Akogbe, M. AOne 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.Item Investigating the Thresholding Effect and Fingerprint Transformation Using Cross-Correlation Similarity Matching(Faculty of Science Lafiya, 2025-01-12) Garuba O.R., Abdullahi, I.M., Dogo, E.M., & Maliki, DOne 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.Item Impact of Gaussian Noise on the Optimization of Medical Image Registration(2024) Sokomba, A. Z.; Dogo, E. M.; Maliki, D.; Abdullahi, I. M.Gaussian noise often poses a significant challenge to medical image registration, impacting the accuracy and reliability of alignment across varying imaging modalities. The research investigates the effect of Gaussian noise on medical image registration by comparing four optimization techniques: a direct approach, an optimization using FMINCON, a multiscale approach, and a combined optimization strategy that integrates FMINCON and the multiscale approach. The comparative analysis assesses each method's robustness against Gaussian noise, evaluating registration accuracy through three key similarity metrics: Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM). The results reveal that while each approach demonstrates a degree of resilience to noise, the combined optimization method significantly outperforms the others, achieving the lowest MSE, highest PSNR, and superior SSIM. These findings suggest that the combined approach effectively enhances the optimization process by leveraging the strengths of both FMINCON and multiscale frameworks, thus providing a more accurate and noise-resistant solution for medical image registration. The analysis highlights the necessity of image filtering techniques to mitigate noise interference and improve the image registration process in clinical applications.