Classification of Brain Tumor MRIs Using a Kernel Support Vector Machine

  • Mahmoud Khaled Abd-Ellah
  • Ali Ismail AwadEmail author
  • Ashraf A. M. Khalaf
  • Hesham F. A. Hamed
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 636)


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.


Brain tumor MRIs classification K-means DWT PCA KSVM 


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Mahmoud Khaled Abd-Ellah
    • 1
  • Ali Ismail Awad
    • 2
    • 3
    Email author
  • Ashraf A. M. Khalaf
    • 4
  • Hesham F. A. Hamed
    • 4
  1. 1.Electronic and Communication DepartmentAl-Madina Higher Institute for Engineering and TechnologyGizaEgypt
  2. 2.Department of Computer Science, Electrical and Space EngineeringLuleå University of TechnologyLuleåSweden
  3. 3.Faculty of EngineeringAl Azhar UniversityQenaEgypt
  4. 4.Electrical Engineering Department, Faculty of EngineeringMinia UniversityMiniaEgypt

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