A new hybrid method combining genetic algorithm and support vector machine classifier: Application to CAD system for mammogram images

Abstract

Breast cancer continues to be one of the most common cancers, and survival rates critically depend on its detection in the initial stages. Several studies have demonstrated the benefits and potential of using CAD (Computer-Assisted Diagnosis) systems to help specialists in their clinical interpretation of mammograms. CAD is based essentially on 2 main steps: Extraction of pertinent features and classification. In fact, several types of features are used in this work characterizing the extracted masses which are: texture features based on co-occurrence matrix and shape features based on Hu moments and central moments. All these features represent feature vector used in training and testing the used classifier. To reduce dimensionality and optimize classification process a new approach based on genetic algorithm is proposed. It incorporates Svm classifier results as part of multi objective function for fitness function. Once the best subset of features is chosen, classification is made by SVM classifier using Gaussian kernel function. Experimental results demonstrate the effectiveness of the proposed algorithm.