Open AccessA research about breast cancer detection using different neural networks and K-MICA algorithm
Author(s) -
AA Kalteh,
Payam Zarbakhsh,
Meysam Jirabadi,
Abdoljalil Addeh
Publication year - 2013
Publication title -
journal of cancer research and therapeutics/journal of cancer research and therapeutics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.475
H-Index - 39
eISSN - 0973-1482
pISSN - 1998-4138
DOI - 10.4103/0973-1482.119350
Subject(s) - artificial neural network , breast cancer , cluster analysis , computer science , pattern recognition (psychology) , artificial intelligence , classifier (uml) , euclidean distance , perceptron , algorithm , probabilistic neural network , k means clustering , multilayer perceptron , machine learning , data mining , cancer , time delay neural network , medicine
Breast cancer is the second leading cause of death for women all over the world. The correct diagnosis of breast cancer is one of the major problems in the medical field. From the literature it has been found that different pattern recognition techniques can help them to improve in this domain. This paper presents a novel hybrid intelligent method for detection of breast cancer. The proposed method includes two main modules: Clustering module and the classifier module. In the clustering module, first the input data will be clustered by a new technique. This technique is a suitable combination of the modified imperialist competitive algorithm (MICA) and K-means algorithm. Then the Euclidean distance of each pattern is computed from the determined clusters. The classifier module determines the membership of the patterns using the computed distance. In this module, several neural networks, such as the multilayer perceptron, probabilistic neural networks and the radial basis function neural networks are investigated. Using the experimental study, we choose the best classifier in order to recognize the breast cancer. The proposed system is tested on Wisconsin Breast Cancer (WBC) database and the simulation results show that the recommended system has high accuracy.