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Computerized grading of brain tumors supplemented by artificial intelligence

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Abstract

For effective diagnosis of health conditions, there is a need to process medical images to obtain meaningful information. The diagnosis of brain tumors begins with magnetic resonance imaging (or MRI) scan. This is followed by segmentation of the medical images so obtained which can prove cumbersome if it were to be performed manually. Determining the best approach to do segmentation remains challenge among multiple computerized approaches. This paper combines both the identification and classification of tumors from the MRI results and is backed by a cloud-based framework to provision the same. The phase of extraction of features includes the utilization of a Hadoop framework and Gabor filter along with variations in terms of orientation and scale. Artificial bee colony algorithm and support vector machine classifier have been used to designate the degree of optimal features and categorize the same. The grading of brain tumors from MRI images can be fulfilled by the aforementioned approach. The said approach is believed to deliver promising results in terms of accuracy, which has also been verified experimentally.

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Authors and Affiliations

  1. Faculty of Engineering, Department of Computer Science and Engineering, Christ (Deemed to be University), Bengaluru, Karnataka, 560074, India

    S. K. Aruna

  2. Department of Computer Science and Engineering, MVJ College of Engineering, Bengaluru, Karnataka, 560067, India

    K. Sindhanaiselvan

  3. Department of Computer Science and Engineering, Er Perumal Manimekalai College of Engineering, Hosur, Krishnagiri, Tamilnadu, 635117, India

    B. Madhusudhanan

Authors
  1. S. K. Aruna
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  2. K. Sindhanaiselvan
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  3. B. Madhusudhanan
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Correspondence to S. K. Aruna.

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Communicated by V. Loia.

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Aruna, S.K., Sindhanaiselvan, K. & Madhusudhanan, B. Computerized grading of brain tumors supplemented by artificial intelligence. Soft Comput 24, 7827–7833 (2020). https://doi.org/10.1007/s00500-019-04403-7

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  • DOI: https://doi.org/10.1007/s00500-019-04403-7

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