Parameter Optimization of Support Vector Machine using River Formation Dynamic on Brain Tumor Classification

Abstract

Brain tumor is a condition that can interfece with brain function due to abnormal cell growth in the brain. MRI is used as a diagnostic tool when a patient has a brain tumor. The images obtained through MRI will be analyzed by the doctor to determine the type of tumor. Therefore, it is necessary to have a system that can classify tumor types based on MRI images. The image will be extracted using the HOG method, then classified using SVM. Certain measures can be taken to improve SVM performance, such as optimizing its parameters. This research develops a system that uses a novel combination, the SVM with the River Formation Dynamic (RFD) algorithm. RFD is being used to optimize parameters of SVM (C and gamma). The basic idea of RFD is to imitate the movement of water droplets flowing from high to low areas. This research compares the accuracy produced by SVM with the accuracy produced by SVM-RFD. The result is that SVM-RFD provides the better accuracy than only using SVM. The accuracy result by SVM on the MRI dataset is 74.37%. When we compared it with SVM-RFD, the accuracy increased by 13.19% to 87.56%. Further work will be carried out on the implementation of RFD on other SVM parameters to find other parameter combinations that can improve the accuracy of SVM.