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An efficient method for brain tumor detection and categorization using MRI images by K-means clustering & DWT

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Abstract

Brain tumor is an uncontrolled mass of tissues in the brain which originate due to mutated growth of tissues. Brain tumor has become a leading cost of death in modern day environment and researchers are inclined to find ways to mitigate the proliferation of this disease. A lot of methods have been applied in brain tumor detection ranging from image processing to signal based analysis. In this study a robust image processing based method is applied using MRI images. MRI images are preferred due to their simplicity and low noise presence. In this study first a clustering based method is used to segment the image and then SVM is applied for tumor detection. A total seven features were considered and were analyzed by the classifiers. SVM with 94.6% accuracy gave a robust result.

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Acknowledgements

The authors of this paper express their warm regards to doctors at Rajendra institute of medical sciences for providing valuable information about this topic.

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Authors and Affiliations

  1. Department of Computer Science, Birla Institute of Technology, Mesra, Ranchi, India

    Atish Chaudhary & Vandana Bhattacharjee

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  1. Atish Chaudhary
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  2. Vandana Bhattacharjee
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Correspondence to Atish Chaudhary.

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Chaudhary, A., Bhattacharjee, V. An efficient method for brain tumor detection and categorization using MRI images by K-means clustering & DWT. Int. j. inf. tecnol. 12, 141–148 (2020). https://doi.org/10.1007/s41870-018-0255-4

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  • DOI: https://doi.org/10.1007/s41870-018-0255-4

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