Multi-cluster support vector machine classifier for the classification of suspicious areas in digital mammograms

This paper presents a novel technique for the classification of suspicious areas in digital mammograms. The proposed technique is based on a novel idea of clustering input data into numerous (soft) clusters and amalgamating them with a Support Vector Machine (SVM) classifier. The technique is called Multi-Cluster Support Vector Machine (MCSVM) and is designed to provide a fast converging technique with good generalization abilities leading to an improved classification as a benign or malignant class. The proposed SCSVM technique has been evaluated on data from the DDSM benchmark database. The experimental results showed that the proposed MCSVM classifier achieves better results than standard SVM. A paired t-test and Anova analysis showed that the results are statistically significant

Multi-cluster support vector machine classifier for the classification of suspicious areas in digital mammograms

This paper presents a novel technique for the classification of suspicious areas in digital mammograms. The proposed technique is based on a novel idea of clustering input data into numerous (soft) clusters and amalgamating them with a Support Vector Machine (SVM) classifier. The technique is called Multi-Cluster Support Vector Machine (MCSVM) and is designed to provide a fast converging technique with good generalization abilities leading to an improved classification as a benign or malignant class. The proposed SCSVM technique has been evaluated on data from the DDSM benchmark database. The experimental results showed that the proposed MCSVM classifier achieves better results than standard SVM. A paired t-test and Anova analysis showed that the results are statistically significant