Brain tumor detection using optimisation classification based on rough set theory

  • T. Rajesh
  • R. Suja Mani Malar
  • M. R. Geetha


Recently computer aided diagnosis is largely used in many clinical processes to detect, predict and analyze many abnormalities. It is clear that in medical image processing, brain tumor classification and detection plays a significant task. MRI gives anatomical structure’s information, and the potential abnormal tissues’ information. Hence in this paper a new system is proposed for detection and classification of brain tumors. The proposed system consists of feature extraction and tumor classification. In feature extraction, Rough set theory (RST) is used and for classification task particle swam optimization neural network (PSONN) is trained and tested in order to classify the MRI brain images into normal and abnormal.


MRI image Feature Extraction Rough set theory Particle swarm optimisation neural network Fuzzy-SVM 


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.PSN College of Engineering and TechnologyTirunelveliIndia
  2. 2.Narayanaguru Siddhartha College of EngineeringPadanthalumooduIndia
  3. 3.Ponjesly College of EngineeringNagercoilIndia

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