Breast Cancer Recognition Using a Novel Hybrid Intelligent Method

Abdoljalil Addeh, Ata Ebrahimzadeh



Breast cancer is the second largest cause of cancer deaths among women. At the same time, it is also among the most curable cancer types if it can be diagnosed early. This paper presents a novel hybrid intelligent method for recognition of breast cancer tumors. The proposed method includes three main modules: the feature extraction module, the classifier module and the optimization module. In the feature extraction module, fuzzy features are proposed as the efficient characteristic of the patterns. In the classifier module, because of the promising generalization capability of support vector machines (SVM), a SVM based classifier is proposed. In support vector machine training, the hyper-parameters have very important roles for its recognition accuracy. Therefore, in the optimization module, bees algorithm (BA) is proposed for selecting of appropriate parameters of the classifier. The proposed system is tested on Wisconsin Breast Cancer (WBC) database and simulation results show that the recommended system has a high accuracy.


Breast cancer; Fuzzy clustering; BA; SVM; WBC

Full Text:



  • There are currently no refbacks.