Fuzzy‐Based Segmentations Performance Analysis for Breast Tumor Detection Using Spatial Fuzzy C‐Means Filtering with Preconditions (SFCM‐P) Over Bilateral Fuzzy K‐Mean Clustering Algorithm (BiFKC)
K. Surya Prakash, Dakshinamurthy Sungeetha · 2025
This study aims to compare the breast tumor detection accuracy of the innovative spatial fuzzy c-means filtering with preconditions (SFCM-P) algorithm with that of the bilateral fuzzy k-mean clustering (BiFKC) algorithm, which is used for disease classification in image feature extraction. There are a total of 40 participants divided into two groups: Group 1 uses SFCM-P to assess the accuracy of breast tumor detection, and Group 2 uses BiFKC. Each group has a sample size of 20, with a pretest power of 80% and an error rate of 0.04. The results show that compared to BiFKC, the innovative SFCM-P–based image feature extraction technique achieves a much higher accuracy of 91.85%. A value of 0.032 (p<0.05) was used to determine statistical significance. In conclusion, the accuracy of the image feature extraction system based on BiFKC is much lower than that of the innovative SFCM-P method.