Hybrid Image Mining Methods to Classify the Abnormality in Complete Field Image Mammograms Based on Normal Regions

Breast Cancer now becomes a common disease among woman in developing as well as developed countries. Many noninvasive methodologies have been used to detect breast cancer. Computer Aided diagnosis through, Mammography is a widely used as a screening tool and is the gold standard for the early detection of breast cancer. The classification of breast masses into the benign and malignant categories is an important problem in the area of computer-aided diagnosis of breast cancer. We present a new method for complete total image of mammogram analysis. A mammogram is analyzed region by region and is classified as normal or abnormal. We present a hybrid technique for extracting features that can be used to distinguish normal and abnormal regions of a mammogram. We describe our classifier technique that uses a unique re-classification method to boost the classification performance. Our proposed hybrid technique comprises decision tree followed by association rule miner shows most proficient and promising performance with high classification rate compared to many other classifiers. We have tested this technique on a set of ground-truth complete total image of mammograms and the result was quite effective

Breast Cancer Detection using Recursive Least Square and Modified Radial Basis Functional Neural Network

A new approach for classification has been presented in this paper. The proposed technique, Modified Radial Basis Functional Neural Network (MRBFNN) consists of assigning weights between the input layer and the hidden layer of Radial Basis functional Neural Network (RBFNN). The centers of MRBFNN are initialized using Particle swarm Optimization (PSO) and variance and centers are updated using back propagation and both the sets of weights are updated using Recursive Least Square (RLS). Our simulation result is carried out on Wisconsin Breast Cancer (WBC) data set. The results are compared with RBFNN, where the variance and centers are updated using back propagation and weights are updated using Recursive Least Square (RLS) and Kalman Filter. It is found the proposed method provides more accurate result and better classification