A Hybrid Of ACO-FFA Algorithm For Feature Selection In Digital Mammogram

Digital mammogram is the only effective screening method to detect the breast cancer. Gray level co-occurrence matrix (GLCM) textural features are extracted from the mam-mogram. All the features are not essential to detect the mammogram. Therefore identify-ing the relevant feature is the aim of this work. Feature selection improves the classifica-tion rate and accuracy of any classifier. In this paper a new hybrid metaheuristic named ACO-FFA a hybrid of Ant Colony Optimization (ACO) and Firefly Algorithm (FFA) is proposed for feature selection in Digital Mammogram. ACO is a good metaheuristic op-timization technique but the drawback of this algorithm is that the ant will walk through the path where the pheromone density is high which makes the whole process slow hence FFA is employed to carry out the local search of ACO. Support Vector Machine (SVM) classifier with Radial Basis Kernel Function (RBF) is done along with the ACO to classi-fy the normal mammogram from the abnormal mammogram. Experiments are conducted in mini-MIAS database. The performance of the new hybrid algorithm is compared with the ACO and PSO algorithm. The results show that the hybrid ACO-FFA algorithm is more accurate than the other techniques.