Performance Analysis of Em, Svd and Svm Classifiers in Classification of Carcinogenic Regions of Medical Images

摘要

In this paper, the performance analysis of classifiers for classification of carcinogenic regions from various medical images is carried out. Expectation Maximization (EM), Singular Value Decomposition (SVD) and Support Vector Machines (SVM) are well thought-out for analysis. EM classifier performs as the optimizer and SVD classifier performs as the dual class classifier. SVM classifier is used as both optimizer and classifier for multiclass classification procedure and for wide stage cancer detection procedures. The performance analysis of all the three classifiers are analyzed for a group of cancer patients based on the benchmark parameter such as perfect classification, sensitivity, specificity, false positive rate, false negative rate and missed classification. From the experimental results it is evident, that the SVM classifier significantly outperforms other classifiers in the classification of carcinogenic regions of medical images.