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Brain Tumor Classification Using Principal Component Analysis and Kernel Support Vector Machine

  • Richard Torres-Molina
  • Carlos Bustamante-Orellana
  • Andrés Riofrío-Valdivieso
  • Francisco Quinga-Socasi
  • Robinson Guachi
  • Lorena Guachi-GuachiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11872)

Abstract

Early diagnosis improves cancer outcomes by giving care at the most initial possible stage and is, therefore, an important health strategy in all settings. Gliomas, meningiomas, and pituitary tumors are among the most common brain tumors in adults. This paper classifies these three types of brain tumors from patients; using a Kernel Support Vector Machine (KSVM) classifier. The images are pre-processed, and its dimensionality is reduced before entering the classifier, and the difference in accuracy produced by using or not pre-processing techniques is compared, as well as, the use of three different kernels, namely linear, quadratic, and Gaussian Radial Basis (GRB) for the classifier. The experimental results showed that the proposed approach with pre-processed MRI images by using GRB kernel achieves better performance than quadratic and linear kernels in terms of accuracy, precision, and specificity.

Keywords

KSVM Brain tumor classification Image processing 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Yachay Tech UniversityUrcuquíEcuador
  2. 2.SDAS Research GroupUrcuquíEcuador
  3. 3.Department of MechatronicsUniversidad Internacional del EcuadorQuitoEcuador
  4. 4.Department of Mechanical and Aerospace EngineeringSapienza University of RomeRomeItaly

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