Mammogram Classification Using ANFIS with Ant Colony Optimization Based Learning
K. Thangavel, Abubacker Kaja Mohideen · Communications in computer and information science · 2016
Women Breast Cancer has high incidence rate in worldwide. Computer aided diagnosis helps the radiologist to diagnose and treat the breast cancer at early stage. Recent studies states that Adaptive Neural Fuzzy Inference System (ANFIS) classifier achieves notable performance than the other classifiers. The major forte of ANFIS is that it has the robust learning mechanism with fuzzy data. However, the connections between the layers are not pruned for their significance. An Ant Colony Optimization (ACO) based learning is proposed in this paper to improve ANFIS classifier with a novel pruning strategy. The proposed algorithm is inspired from the social life of a special species called ‘Weaver Ants’. The proposed classifier is evaluated with the mammogram images from MIAS database, the quantified results show that this weaver ant based learning strategy improves the ANFIS classifier’s performance. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.