An integrated method of associative classification and neuro-fuzzy approach for effective mammographic classification

Computer-aided diagnosis has gained a significant attention in helping radiologists to improve the accuracy of mammographic detection and diagnostic decision. The aim of proposed research work is to build an efficient and accurate classifier for the classification of mammogram images using a hybrid method by incorporating Genetic Association Rule Miner (GARM) with the learning capability of neural network. A set of features is extracted that comprises of 34 features based on the second and third level of the wavelet decomposition with 13 features measured directly from the gray-level co-occurrence matrix. In order to eliminate the inappropriate features and to increase the efficiency mining process, a multivariate filter is used for feature selection. Based on the selected features, an association rule mining based on modified GARM is used to generate association rules. In the classification phase, the newly generated association rules are used as the input for the creation and training of an artificial neural network. Furthermore, an extended associative classifier using fuzzy feed-forward backpropagation neural network (ACFNN) is proposed as an effective classifier in the context of mammography. The proposed ACFNN performance is compared with associative classifier using feed-forward backpropagation neural network (ACNN). Based on the experimental results, the performance of the proposed ACFNN is improved significantly. Furthermore, it can be inferred that the mammogram classification is better by using ACFNN with accuracy of 95.1 % as compared to ACNN with 93.7 %.