Multi Class Brain Cancer Prediction System Empowered with BRISK Descriptor  

作　　者：Madona B.Sahaai G.R.Jothilakshmi E.Praveen V.Hemath Kumar 

机构地区：[1]Department of Electronics and Communication Engineering,VISTAS,Chennai,Tamil Nadu,India

出　　处：《Intelligent Automation & Soft Computing》2023年第5期1507-1521,共15页智能自动化与软计算（英文）

摘　　要：Magnetic Resonance Imaging(MRI)is one of the important resources for identifying abnormalities in the human brain.This work proposes an effective Multi-Class Classification(MCC)system using Binary Robust Invariant Scalable Keypoints(BRISK)as texture descriptors for effective classification.Atfirst,the potential Region Of Interests(ROIs)are detected using features from the acceler-ated segment test algorithm.Then,non-maxima suppression is employed in scale space based on the information in the ROIs.The discriminating power of BRISK is examined using three machine learning classifiers such as k-Nearest Neighbour(kNN),Support Vector Machine(SVM)and Random Forest(RF).An MCC sys-tem is developed which classifies the MRI images into normal,glioma,meningio-ma and pituitary.A total of 3264 MRI brain images are employed in this study to evaluate the proposed MCC system.Results show that the average accuracy of the proposed MCC-RF based system is 99.62%with a sensitivity of 99.16%and spe-cificity of 99.75%.The average accuracy of the MCC-kNN system is 93.65%and 97.59%by the MCC-SVM based system.

关 键 词：Braincancer BRISKdescriptor randomforest multi-classclassification brain image analysis 

分 类 号：R739.41[医药卫生—肿瘤]