Breast Cancer Detection using Crow Search Optimization based Intuitionistic Fuzzy Clustering with Neighborhood Attraction

Abstract

Objective: Generally, medical images contain lots of noise that may lead to uncertainty in diagnosing the<br />abnormalities. Computer aided diagnosis systems offer a support to the radiologists in identifying the disease affected<br />area. In mammographic images, some normal tissues may appear to be similar to masses and it is tedious to differentiate<br />them. Therefore, this paper presents a novel framework for the detection of mammographic masses that leads to<br />early diagnosis of breast cancer. Methods: This work proposes a Crow search optimization based Intuitionistic fuzzy<br />clustering approach with neighborhood attraction (CrSA-IFCM-NA) for identifying the region of interest. First order<br />moments were extracted from preprocessed images. These features were given as input to the Intuitionistic fuzzy<br />clustering algorithm. Instead of randomly selecting the initial centroids, crow search optimization technique is applied<br />to choose the best initial centroid and the masses are separated. Experiments are conducted over the images taken from<br />the Mammographic Image Analysis Society (mini-MIAS) database. Results: CrSA-IFCM-NA effectively separated<br />the masses from mammogram images and proved to have good results in terms of cluster validity indices indicating<br />the clear segmentation of the regions. Conclusion: The experimental results show that the accuracy of the proposed<br />method proves to be encouraging for detection of masses. Thus, it provides a better assistance to the radiologists in<br />diagnosing breast cancer at an early stage.