Liver cancer identification based on PSO-SVM model

This paper proposes a novel liver cancer identification method based on PSO-SVM. First, the region of interest (ROI) is determined by Lazy-Snapping, and various texture features are extracted from ROI. Afterwards, F-score algorithm is applied to select relevant features, based on which liver cancer classifier is designed by combining parallel Support Vector Machine (SVM) with Particle Swarm Optimization (PSO) algorithm. PSO is used to automatically choose parameters for SVM, and the advantage is that it makes the choice of parameter more objective and avoids the randomicity and subjectivity in the traditional SVM whose parameters are decided through trial and error. The experiment results on real-world datasets show that the proposed parallel PSO-SVM training algorithm improves the prediction accuracy of liver cancer.