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Volume 6, Issue 1, March 2019

Classification of Tumors using PCA and SVM from MRI



  • Abstract

    Classification of tumor tissues in magnetic resonance images (MRI) is a significant task but is relatively time overwhelming while achieved manually by proficient. Automating this progression is difficult due to the high miscellany in exterior of tumor tissue in different patients, and in many cases, resemblance between tumor tissues and normal tissues. This paper presents an automatic technique for classification of tumor tissues in MRI. After pre-processing and elimination of the regions that do not have useful information, it creates a projection on PF affected tumor region, when pre-processing and elimination of the regions that do not have useful information was done. In this work feature extraction from MR Images is agreed by Daubachies(DAUB-4) Wavelet technique. Using Principal Component Analysis (PCA), to choice the unsurpassed features for classification. Support Vector Machine (SVM) takes the selected features as an input from PCA. This enhances the tumor boundaries more and is very fast when compared to many other classification algorithms.

  • Keywords

    PCA, MRA and MRI

  • Author Affiliations



    1. Srividya college of Engg & Tech, Virudhunagar-626005
    2. National Engineering college, Kovilpatti-629005
  • Dates

    Manuscript received : 12 December 2018
    Manuscript revised   : 16 January 2019
    Accepted                     : 23 February 2019

  • Cite this article as:
    K.Palraj, V.Kalaivani, Int.j.of IJACCS(2019)


    Print ISSN           : 2347 - 9280
    Online ISSN        : 2347 - 9299
    Publisher Name  : Sri Eshwar Publications, Coimbatore, Tamilnadu, India.

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