Performance analysis of EM, SVD, and SVM classifiers in classification of carcinogenic regions of medical images

Abstract

ABSTRACTIn this article, the performance analysis of Expectation Maximization (EM), Singular Value Decomposition (SVD), and Support Vector Machines (SVM) classifiers for classification of carcinogenic regions from various medical images is carried out. Cancer detection is one of the critical issues where excessive care needs to be taken for better diagnosis. Any classifier needs to detect the cancer with respect to the efficiency in time of detection and performance. Due to these, three classifiers are selected: Expectation Maximization (EM), Singular Value Decomposition (SVD), and Support Vector Machines (SVM). 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 100 cancer patients based on the benchmark parameter such as Performance Measures and Quality Metrics. From the experimental results it is evident, that the SVM classifier significantly outperforms other classifiers in the classification of carcinogenic regions of medical images.