Identifying the Brain Tumors and Classified Using a New Approach with The Support of Random Forest Decision Tree

Authors(3) :-Dr. P. Kalyani, Dr. D. Murali, T. Munireddy

Now-a-days, most of the people suffered from Brain tumor. In the whole nervous system, human brain is the one of the most important organ. By these brain tumors most of the people lost their life. There are extra-ordinary cells inside the brain leads to brain tumors. The brain tumors are of different like malignant tumors or cancerous tumors and benign tumors. In this proposed, a technique is used consists of preprocessing, segmentation, Feature extraction and Classification. Here, we are segments the tumors and detects and classified the tumors based on improved RFDT approach. The main thing we focus to investigate the tumors in early stages which help for the health practitioners.

Authors and Affiliations

Dr. P. Kalyani
Professor, CSE Department, Vemu Institute of Technology, P. Kothakota, Chitoor District, Andhra Pradesh, India
Dr. D. Murali
Professor, CSE Department, Vemu Institute of Technology, P. Kothakota, Chitoor District, Andhra Pradesh, India
T. Munireddy
Research Scholar, ECE Department, SSSUTMS, Bhopal, Andhra Pradesh, India

MRI images, Brain tumor, Segmentation, Random Forest Decision Tree.

  1. Prof. A.R. Kavitha, L.Chitra, R.Kanaga, "Brain Tumor segmentation using Genetic Algorithm with SVM Classifer", International Journal of Advanced Research in Electrical, Electronics & Instrumental Engineering, Vol.5, Issue, March 2016.
  2. Kamal Kant Hiran, RuchiDoshi, "An Artificial Neural Network Approach for brain tumor detection using Digital Image segmentation ". International journal of Emerging trends and Techonolgy in Computer Science. Vol.2,pp.1-15,2013.
  3. Kimia rezaei and Hamed agahi," Malignant & Benign Brain Turnor Segmentation & classification using SVM with weighted kernel width",journel(SIPIJ) vol.8.
  4. A.R.Kavitha, L.Chitra , R.Kanaga,"Brain Turnor segmentation using Generic Algorithm with SVM classifier ",International journal of Advanced Research in Electrical, Electronics & instrumentation engineering, Vol.5, Issue 3, March 2016.
  5. Jahnavi M.S., Kurup S., "A Novel Approach to detect brain Tumor in MRI Images using hybrid techniques with SVM classifiers",2016 IEEE international conference on Recent Trends in Electronics, Information & Communication Technologies(RJEICT), Banglore, India,PP.546-549.
  6. Praveen,Amritpal singh., "Detection of Brain Tumor in MRI Images using combination of Fuzzy C-means & SVM ", 2nd international conference on signal processing & Integrated Network(SPIN), 2015,98-102.
  7. Shahid Eqbal and M.A.Ansari "Wavelet Based Medical Image Feature Extraction By Segmentation Using FCM and SVM", International Advanced Research Journal in science, Engineering and Technology (IARJSET), vol.4, issue 9,Sept. 2017, pp.252-257.
  8. Janki Naik , Prof. Sagar Patel "Tumor Detection and Classification using Decision Tree in Brain MRI ",International Journal Of Engineering Development and Research (IJEDR), ISSN: 2321-9939

Publication Details

Published in : Volume 3 | Issue 6 | July-August 2018
Date of Publication : 2018-07-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 270-273
Manuscript Number : CSEIT183667
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Dr. P. Kalyani, Dr. D. Murali, T. Munireddy, "Identifying the Brain Tumors and Classified Using a New Approach with The Support of Random Forest Decision Tree", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 6, pp.270-273, July-August-2018.
Journal URL : http://ijsrcseit.com/CSEIT183667

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