Immune Feature Weighted Least-Squares Support Vector Machine for Brain Tumor Detection Using MR Images

Brain tumor is one of the leading causes of death making tumor detection very important and challenging in the medical field. This paper describes tumor detection in medical images using immune feature weighted least squares-support vector machine (IFWLS-SVM). The challenge in brain tumor detection in magnetic resonance (MR) images is the existence of non-linearity in real data. Least squares-support vector machine (LS-SVM) is a conventional algorithm that has been applied to diagnose the detection problems in MR images and non-linear distribution in brain tumors. LS-SVM solves a linear system for a training algorithm instead of using quadratic programming in SVM. In conventional LS-SVM, each sample feature taken has equal importance for classification results, which does not give accurate results in real applications. In addition, parameters of LS-SVM and their kernel function prominently affect the classification result. An IFWLS-SVM has been used to optimize the kernel and tune the parameters of LS-SVM in this paper. Theoretical analysis and experimental results showed that IFWLS-SVM has better performance than other conventional algorithms.