BI-RADS Assessment Categorization for Breast Cancer Using Self-Adaptive Particle Swarm Optimization
Keywords:
Mammography, Computer-aided Diagnosis, BI-RADS Assessment, Particle Swarm Optimization, Breast CancerAbstract
Mammography is now the gold standard for screening breast cancer. However, approximately 70% of unnecessary biopsies are benign because of the low positive predictive value of mammography interpreted breast biopsies. Computer-aided diagnosis (CAD) technologies may be utilized to reduce the needlessness of breast biopsies. This device helps physicians decide whether to perform a breast biopsy on a suspicious tumor detected during mammography. The suggested CAD system utilizes clustering theory in conjunction with swarm intelligence to classify mass lesions as benign or malignant based on patient age and three BI-RADS attributes: mass shape, mass density and mass margin.The exploration capacity of natural computing algorithms is mostly defined by how diverse their inputs are. When variety is lost too quickly, poor convergence is inevitable. It is also true that, with nearly every paradigm of natural computing, there are number-sensitive parameters accessible, whose ideal values dictate the quality of the solution. In this research, we apply diversity-based self-adaptive clustering to PSO to improve the quality of the resulting clusters. Many variables, including the inertia constant, the social constant, and the cognitive constant, change depending on the current level of variety.
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