An Advanced Breast Tumor Classification Algorithm

Abstract

Classifying breast malignancy based on their shape is very difficult and challenging task. There are two different types of breast tumor named as benign and malignant. Benign tumors are well defined and round or oval structured one while malignant tumors are ill defined and irregular structured one. In this paper, a novel best fitting (BSF) algorithm is proposed for classifying breast malignancy. In this algorithm centroid of a contour is calculated and used as a center of best fitting circle whose radius is calculated as arithmetic mean of minimum and maximum of radial distance which is measured from centroid of the tumor. Entropy (E), normalized mean radial distance (Nr) of a given tumor and similarity between best fitting circle and tumor is measured in terms of variation (σ) are the feature (F1,F2, and F3) for classification of breast tumors. The performance of each parameter and its combinations for all 150 contours are measured and evaluated through receiver operating characteristics (ROC). The necessary datasets are taken from Rangayan database as well as our local dataset for validation and verification