A Novel Approach for Brain Tumor Classification using Bilateral Filtering and Cascade RF-SVM

Abstract

This work introduces a new approach to classifying brain tumors that integrates various techniques to maximize classification performance.  Bilateral filtering is applied as pre-processing in the initial stage to remove noise from medical images without degrading the borders of tumor areas. In order to isolate the textural characteristics of the tumor, which play a key role in differentiating the types, the Local Binary Pattern (LBP) technique was utilized. The Improved Grey Wolf Optimization (GWO) algorithm was utilized for optimizing the feature set by removing redundant features in order to improve classification. A Random Forest classifier is employed for removing irrelevant features, and finally, the classification is performed by a Support Vector Machine classifier. This is stage one in classification. With the method proposed within this paper, brain tumors are correctly classified with impressive 99.3 percent accuracy. The very high accuracy suggests the robustness of the union of strong classification methods and novel feature selection methods. Apart from its use in imagine of brain tumors, the method can be applied to a broad variety of other medical image problems to make diagnosis highly efficient and credible.