An analytical research study of MRI brain tumor modalities and classification techniques

  • Authors

    • Deepthi Murthy T S Assistant Professor, REVA University
    • Sadashivappa G 2Professor, Department of Telecommunication Engineering, RV College of Engineering, Bangalore
    2019-09-20
    https://doi.org/10.14419/ijet.v8i3.29561
  • Brain Tumor, Classification, Feature Extraction, MRI Image Modalities, SVM, Segmentation, Tumor Detection.

  • Abstract

    In MRI image analysis, brain cancer or tumor analysis is the challenging task for the doctors due to the complex structure of the human brain and high assortment in the appearance of cancerous tissues. At present brain tumor detection and its diagnosis is very essential to reduce the death rate of brain cancer patients. The brain tumor recognition process can be performed by various standard image processing techniques for e.g. MRI (magnetic resonance imaging), ECG (Electro-Encephalography) and many more. Among these, MRI imaging is the emerging tumor detection technique. The efficiency of the tumor detection process provides anatomical knowledge about cancerous tissues in the MRI brain, which helps the doctors for tumor diagnosis. The comprehensive survey study provides different MRI brain tumor detection and classification techniques based on WHO grade system report and different imaging modalities. The classification taxonomy is presented based on segmentation and feature extraction methods. Based on the prior research study, have mainly focused on different MRI imaging modality and evaluated performance and classification accuracy. The last section of the survey study mainly highlighting research challenges which could help for future research in MRI brain tumor detection and classification techniques.

     

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  • How to Cite

    Murthy T S, D., & G, S. (2019). An analytical research study of MRI brain tumor modalities and classification techniques. International Journal of Engineering & Technology, 8(3), 357-376. https://doi.org/10.14419/ijet.v8i3.29561

    Received date: 2019-06-28

    Accepted date: 2019-08-25

    Published date: 2019-09-20