Feature weighting for cancer tumor detection in mammography images using gravitational search algorithm
Optimization methods have been widely used in image processing and computer vision. In this paper, k nearest neighbor (KNN) and real valued gravitational search algorithm (RGSA) are used to detect the breast cancer tumors in mammography images. GSA is used as a tool for optimization of the features weighting (FW) and tuning the classifier. FW KNN based on GSA is employed to enhance the K NN classification accuracy. The weighted features and the tuned K NN classifier are utilized for detecting tumors. The obtained results show good efficiency of GSA based FW KNN classification for breast cancer tumor detection.
2016 6th International Conference on Computer and Knowledge Engineering (ICCKE)