Research Article Open Access

PERFORMANCE ANALYSIS OF BRAIN TUMOR DIAGNOSIS BASED ON SOFT COMPUTING TECHNIQUES

P. Shantha Kumar1 and P. Ganesh Kumar2
  • 1 Department of Computer Applications, RVS College of Engineering and Technology, Dindigul, India
  • 2 Department of Information Technology, KLN College of Engineering, Madurai, India

Abstract

Computed tomography images are widely used in the diagnosis of brain tumor because of its faster processing, avoiding malfunctions and suitability with physician and radiologist. This study proposes a new approach to automated detection of brain tumor. This proposed work consists of various stages in their diagnosis processing such as preprocessing, anisotropic diffusion, feature extraction and classification. The local binary patterns and gray level co-occurrence features, gray level and wavelet features are extracted and these features are trained and classified using Support vector machine classifier. The achieved results and quantitatively evaluated and compared with various ground truth images. The proposed method gives fast and better segmentation and classification rate by yielding 99.4% of sensitivity, 99.6% of specificity, 97.03% of positive predictive value and 99.5% of overall accuracy.

American Journal of Applied Sciences
Volume 11 No. 2, 2014, 329-336

DOI: https://doi.org/10.3844/ajassp.2014.329.336

Submitted On: 9 November 2013 Published On: 27 December 2013

How to Cite: Kumar, P. S. & Kumar, P. G. (2014). PERFORMANCE ANALYSIS OF BRAIN TUMOR DIAGNOSIS BASED ON SOFT COMPUTING TECHNIQUES. American Journal of Applied Sciences, 11(2), 329-336. https://doi.org/10.3844/ajassp.2014.329.336

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Keywords

  • Tumor
  • Classifier
  • Segmentation
  • Classification