Non-invasive grading of brain tumors using online support vector machine with dynamic fuzzy rule-based parameters optimization

Abstract

Non-invasive grading of brain tumors provides a valuable understanding of tumor growth that helps choose the proper treatment. In this paper, an online method with an innovative optimization approach as well as a new and fast tumor segmentation method is proposed for the fully automated grading of brain tumors in magnetic resonance (MR) images. First, the tumor is segmented based on two characteristics of the tumor appearance (intensity and edges information). Second, the features of the tumor region are extracted. Then, the online support vector machine with the kernel (OSVMK) by dynamic fuzzy rule-based optimization of the parameters is used for the grading of tumors. The performance evaluation of the proposed tumor segmentation method was performed by manual segmentation using similarity criteria. Also, tumor grading results compared the proposed online method, the conventional online method, and the batch SVM with the kernel (batch SVMK) in terms of accuracy, precision, recall, specificity, and execution times. The segmentation results show a good correlation between the tumor segmented by the proposed method and by experts manually. Also, the grading results based on the accuracy, precision, recall, and specificity, 95.20%, 97.87%, 96.48%, and 96.45%, respectively, indicate the acceptable performance of the proposed method. The execution times of the introduced online method are much less than the batch SVMK. The method demonstrates the potential of fully automated tumor grading to provide a non-invasive diagnosis in order to determine the treatment strategy for the disease. So the physicians, according to the tumor’s grade, can match the treatment of the brain tumor to the patient’s individual needs and thus make the best course of treatment for each patient.