Brain Tumor Disease Identification Using GNSWF – Based Feature Extraction and Random Forest Classifiers

Abstract: Now a days, detection and identification of brain tumor we use an image processing techniques for a segmentation of image and RFT are use to classification of tumor result. Using RFT segmented image is divided into nodes, here we detect true and false nodes and according to true node design a tree, generated tree nodes are change according to user selection, then the leap nodes are combining and generated a resulted tree. Bagging and Boosting algorithm are use to improve the RFT result. Generated result is in form of complex tree so we use a bagging and boosting. Final result is in the form of three condition normal pre and post condition. In the existing system neural network is use for result but in neural network result in the form of 0 and 1 so noise is present in result. Hence using the RFT system improves the result and performance with great accuracy because the result in the number of digits like 0.02354689 also the result is in plus and minus. They represent the result according to nodes RFT calculate.

Keywords: RFT; Brain tumor; Filters; edges; canny filters.