Brain Tumor Classification Using Pattern Recognition Techniques: the Comprehensive Review

Authors

  • Rahul B. Mapari  Maharashtra Institute of Technology, Aurangabad, Maharashtra, India
  • Dr. Anilkumar N. Holambe  TPCT College of Engineering, Osmanabad, Maharashtra, India

Keywords:

Brain Tumor, Segmentation, Classification, Feature Extraction, Discrete Wavelet Transform, Discrete Cosine Transform, SVM, Probabilistic Neural Network, Artificial Neural Network, MRI

Abstract

A Brain tumor is very serious disease causing deaths of many individuals. The detection and classification system must be available so that it can be diagnosed at early stages. Detection of the brain tumor and its type in its early stage is essential for right treatment. So classification of brain tumor is very important. Tumor classification has been one of the most challenging tasks in clinical diagnosis. Different image processing techniques such as image segmentation, image enhancement and feature extraction are used for detection of the brain tumor in the MRI images of the cancer affected patients. Medical Image Processing is the fast growing and challenging field now days. Image processing and neural network techniques are used to improve the performance of detecting and classifying brain tumor in MRI images. The objective of this review paper is to presents a comprehensive overview for MRI brain tumor segmentation methods. In this paper, various segmentation techniques have been discussed. Comparative analysis of existing techniques has been done in brief.

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International Journal of Scientific Research in Science and Technology

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Published

2018-02-28

Issue

Volume 4, Issue 2, January-February-2018

Section

Research Articles

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How to Cite

[1]
Rahul B. Mapari, Dr. Anilkumar N. Holambe, " Brain Tumor Classification Using Pattern Recognition Techniques: the Comprehensive Review, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 4, Issue 2, pp.730-738, January-February-2018.