Brain tumor classification using weighted least square twin support vector machine with fuzzy hyperplane

Abstract

Brain tumor is an aberrant growth of cells in the brain and represents one of the most lethal cancers around the world. The advanced machine learning models, like twin support vector machine have effectively addressed brain tumor classification tasks with promising results. However, despite its success, it lacks efficient learning as it solves a pair of quadratic programming problems and struggles to distinguish between support vectors and noises. To address these challenges, a novel multi-class classification model based on least square twin support vector machine and fuzzy concepts is formulated. It uses both membership and non-membership weights and integrates local neighborhood information among data points according to their importance. Moreover, to capture the uncertainty in the dataset, the proposed method computes a fuzzy hyperplane, taking all the parameters as fuzzy variables. Further, the model’s efficiency is enhanced by solving a system of linear equations only rather than solving a quadratic programming problem. To show the effectiveness of the proposed algorithm, the numerical experiments on 15 benchmark datasets in terms of average accuracy, $F$-measure, and training time are illustrated. The findings shows that the proposed technique outperforms other baseline models by achieving average accuracy of 88.79% with a linear kernel and 91.71% with a non-linear kernel. The proposed method is also applied to classify brain tumors into four different classes, achieving an average accuracy of 93.45%, which proves its outstanding performance. Moreover, the Friedman and Wilcoxon signed-rank statistical tests are used to confirm the method’s robustness and generalization capability.