Entropy-Based Breast Cancer Detection in Digital Mammograms Using World Cup Optimization Algorithm

Breast cancer is one of the deadliest cancers for women. Early detection of skin cancer gives a high chance for the women to escape from the malady and obtain a cure at the initial stages. In other words,earlydetectionofbreastcancerhasadirectrelationbythewomen’squalityoflife.Inthiscase, mammography images are important. Indeed, the main test used for screening and early diagnosis of breastcancerismammography.Inrecentyears,computer-aidedcancerdetectionhasbeenturnedinto an active field of research and showed a promising future. In this study, a new optimization algorithm based on thresholding is introduced. A WCO algorithm is employed as the optimization algorithm. WCO is a new meta-heuristic approach which is inspired by the FIFA world cup challenge. The presented method utilizes random samples as candidate solutions from the search space inside the image histogram with considering to the objective function that is utilized by the Kapur’s method

Breast Cancer, Entropy, Image Processing, Kapur Thresholding, Mammography, Segmentation, World Cup Optimization