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International Conference on Advanced Machine Learning Technologies and Applications

AMLTA 2018: The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018) pp 343–351Cite as

Computer Aided Diagnostic System for Automatic Detection of Brain Tumor Through MRI Using Clustering Based Segmentation Technique and SVM Classifier

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 723))

Abstract

Due to the acquisition of huge amount of brain tumor magnetic resonance images (MRI) in the clinics, it is very difficult for the radiologists to manually interpret and segment these images within a reasonable span of time. Computer-aided diagnosis (CAD) systems increase the diagnostic abilities of radiologists and reduce the elapsed time for perfect diagnosis. An intelligent computer-aided technique is proposed in this paper for automatic detection of brain tumor from MR images. The proposed technique uses following computational methods; the K-means clustering for segmentation of brain tumor from other brain parts, extraction of features from this segmented brain tumor portion using gray level co-occurrence Matrices (GLCM), and the support vector machine (SVM) to classify input MRI images into normal and abnormal. The whole work is carried out on 64 images consisting of 22 normal and 42 images having brain tumor (benign and malignant). The overall classification accuracy using this method is found to be 99.28% which is significantly good.

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Author information

Authors and Affiliations

  1. EIE Department, SLIET Longowal, Punjab, India

    Atanu K. Samanta & Asim Ali Khan

Authors
  1. Atanu K. Samanta
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  2. Asim Ali Khan
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Corresponding author

Correspondence to Asim Ali Khan .

Editor information

Editors and Affiliations

  1. Faculty of Computers and Information, Information Technology Department, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. Faculty of Computers and Information Sciences, Ain Shams University, Cairo, Egypt

    Mohamed F. Tolba

  3. Faculty of Computers and Information, Department of Computer Science and Engineering, Mansoura University, Dakahlia, Egypt

    Mohamed Elhoseny

  4. Arab Academy for Science, Technology and Maritime Transport (AASTMT), Dokki, Egypt

    Mohamed Mostafa

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Samanta, A.K., Khan, A.A. (2018). Computer Aided Diagnostic System for Automatic Detection of Brain Tumor Through MRI Using Clustering Based Segmentation Technique and SVM Classifier. In: Hassanien, A., Tolba, M., Elhoseny, M., Mostafa, M. (eds) The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018). AMLTA 2018. Advances in Intelligent Systems and Computing, vol 723. Springer, Cham. https://doi.org/10.1007/978-3-319-74690-6_34

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  • DOI: https://doi.org/10.1007/978-3-319-74690-6_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-74689-0

  • Online ISBN: 978-3-319-74690-6

  • eBook Packages: EngineeringEngineering (R0)

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