Classification of brain tumor MRIs using a kernel support vector machine

Mahmoud Khaled Abd-Ellah, Ali Ismail Awad, Ashraf A.M. Khalaf, Hesham F.A. Hamed

Research output: Chapter in Book/Report/Conference proceedingConference contribution

31 Citations (Scopus)

Abstract

The use of medical images has been continuously increasing, which makes manual investigations of every image a difficult task. This study focuses on classifying brain magnetic resonance images (MRIs) as normal, where a brain tumor is absent, or as abnormal, where a brain tumor is present. A hybrid intelligent system for automatic brain tumor detection and MRI classification is proposed. This system assists radiologists in interpreting the MRIs, improves the brain tumor diagnostic accuracy, and directs the focus toward the abnormal images only. The proposed computer-aided diagnosis (CAD) system consists of five steps: MRI preprocessing to remove the background noise, image segmentation by combining Otsu binarization and K-means clustering, feature extraction using the discrete wavelet transform (DWT) approach, and dimensionality reduction of the features by applying the principal component analysis (PCA) method. The major features were submitted to a kernel support vector machine (KSVM) for performing the MRI classification. The performance evaluation of the proposed system measured a maximum classification accuracy of 100% using an available MRIs database. The processing time for all processes was recorded as 1.23 seconds. The obtained results have demonstrated the superiority of the proposed system.

Original languageEnglish
Title of host publicationBuilding Sustainable Health Ecosystems - 6th International Conference on Well-Being in the Information Society, WIS 2016, Proceedings
EditorsNilmini Wickramasinghe, Gunilla Widén, Ming Zhan, Pirkko Nykänen, Hongxiu Li, Reima Suomi
PublisherSpringer Verlag
Pages151-160
Number of pages10
ISBN (Print)9783319446714
DOIs
Publication statusPublished - 2016
Externally publishedYes
Event6th International Conference on Well-Being in the Information Society, WIS 2016 - Tampere, Finland
Duration: Sept 16 2016Sept 18 2016

Publication series

NameCommunications in Computer and Information Science
Volume636
ISSN (Print)1865-0929

Conference

Conference6th International Conference on Well-Being in the Information Society, WIS 2016
Country/TerritoryFinland
CityTampere
Period9/16/169/18/16

Keywords

  • Brain tumor
  • DWT
  • K-means
  • KSVM
  • MRIs classification
  • PCA

ASJC Scopus subject areas

  • General Computer Science
  • General Mathematics

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