Engineering and Technology Journal

A combining two ksvm classifiers based on True pixel values and Discrete wavelet transform for mri-based brain tumor detection and Classification

Document Type : Research Paper

Authors

1 Electrical Engineering Dept., University of Technology-Iraq, Alsina’a street, 10066 Baghdad, Iraq.

2 Electrical Engineering Dept., University of Technology-Iraq, Alsina’a street, 10066 Baghdad, Iraq

Abstract

The studies on brain tumor detection and classification are continuing to improve
the specialists’ ability in diagnosis. Magnetic Resonance Imaging (MRI) is one of
the most common techniques used to evaluate brain tumors diagnosis. However,
brain tumors diagnosis is a difficult process due to congenital malformations and
possible errors in diagnosing benign from malignant tumors. Therefore, this
research aims to propose an integrated algorithm to classify brain tumors following
two stages using the Kernel Support Vector Machine (KSVM) classifier. First
stage classifies the tumors as normal and abnormal, and the second classifies
abnormal tumors as benign and malignant. The first KSVM employs extraction
features by considering the pixel values to classify images as a shape. In contrast,
the second KSVM uses the Discrete Wavelet Transform (DWT), followed by the
Principal Component Analysis (PCA) technique to extract and reduce features and
improve the model performance. Also, K-means clustering algorithm is used to
segment, isolate and calculate the tumor area. The KSVM classifiers use two
kernels (linear and Radial Basis Function (RBF)). Obtained results showed that
the linear kernel achieved 97.5% accuracy and 98.57% accuracy in the first and
second classifier, respectively. For all linear classifiers, a 100% sensitivity level is
achieved. This work validates the proposed model based on the (K-fold) strategy

Graphical Abstract

Highlights

  • We have proposed an integrated algorithm to classify brain tumors following two stages.
  • We have used the Kernel Support Vector Machine (KSVM) classifier.
  • The linear kernel achieved 97.5% accuracy and 98.57% accuracy in the first and second classifiers.

 

Keywords

References
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Engineering and Technology Journal
Volume 40, Issue 2
Electrical Engineering
, 16 articles
February 2022
Page 322-333
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