Classification of Tumors in Human Brain MRI using Wavelet and Support Vector Machine

The task of classification in recovery systems is to differentiate between normal and abnormal brain. In this paper feature extraction from MR Images is carried out by DAUB-4 Wavelet method. DAUB-4 is an efficient tool for feature extraction because it gives better contrast to an image. Due to better contrast it improves easily hanging signals of an image and reduces the overhead. PCA is used to select the best features for classification. These PCA selected features are given as an input to SVM for classification. In this work we are using two SVM kernel functions which are Linear Kernel and Radial Basis Kernel. Experimental results show that the proposed system have high classification accuracy of 98.7% with Radial Basis Kernel.

• Publication year

  2012

• Venue

  IOSR Journal of Computer Engineering

• Publication date

  Unknown publication date

• Fields of study

  Medicine, Computer Science, Engineering

• Identifiers
  DOI 10.9790/0661-0822531
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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