Development of computer‐aided approach for brain tumor detection using random forest classifier

The nonlinear development of cells in brain region forms the abnormal patterns in brain in the form of tumors. It is necessary to detect and diagnose the brain tumors in an automated manner using computer‐aided approaches at large population areas. The noises in brain magnetic resonance image is detected and reduced as preprocessing steps and then grey level co‐occurrence matrix are now extracted from the preprocessed brain image. In this article, random forest classifier‐based brain tumor detection and segmentation methodology is proposed to classify the brain image into normal or abnormal. The proposed brain tumor detection and segmentation system is analyzed in terms of sensitivity, specificity, false‐positive rate, false‐negative rate, likelihood ratio positive, and likelihood ratio negative.

[1]  Qianjin Feng,et al.  Brain Tumor Segmentation Based on Local Independent Projection-Based Classification , 2014, IEEE Transactions on Biomedical Engineering.

[2]  J. Jayakumari,et al.  Automatic detection of brain tumor based on magnetic resonance image using CAD System with watershed segmentation , 2011, 2011 International Conference on Signal Processing, Communication, Computing and Networking Technologies.

[3]  Christos Davatzikos,et al.  MRI-based classification of brain tumor type and grade using SVM-RFE , 2009, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[4]  Shraddha. P. Dhuma,et al.  An Automatic Brain Tumor Detection And Segmentation Using , 2017 .

[5]  Jing Shen,et al.  An Effective Adaptive Median Filter Algorithm for Removing Salt & Pepper Noise in Images , 2010, 2010 Symposium on Photonics and Optoelectronics.

[6]  Emre Dandil,et al.  Computer-Aided Diagnosis of Malign and Benign Brain Tumors on MR Images , 2014, ICT Innovations.

[7]  Nilesh Bhaskarrao Bahadure,et al.  Image Analysis for MRI Based Brain Tumor Detection and Feature Extraction Using Biologically Inspired BWT and SVM , 2017, Int. J. Biomed. Imaging.

[8]  Victor Alves,et al.  Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images , 2016, IEEE Transactions on Medical Imaging.

[9]  Abdel-Badeeh M. Salem,et al.  An Automatic Classification of Brain Tumors through MRI Using Support Vector Machine , 2016 .

[10]  Faouzi Benzarti,et al.  Segmentation of brain MRI using active contour model , 2017, Int. J. Imaging Syst. Technol..

[11]  Pratibha Sharma,et al.  Application of Edge Detection for Brain Tumor Detection , 2012 .

[12]  K. Batri,et al.  Automated detection of glioblastoma tumor in brain magnetic imaging using ANFIS classifier , 2016, Int. J. Imaging Syst. Technol..