Hybrid method for brain tumor extraction and MRI scan classification

  • Authors

    • Moatasem Mohammed Elsayed
    • Abeer Twakol Khalil
    • Tamer Omar Mohamed Diab
    • Ashraf Shawky Selim S. Mohra
    • . .
    https://doi.org/10.14419/ijet.v7i4.27944
  • Brain Tumor, Histogram Processing, Morphological Operations, MRI Scan Classification, Segmentation.

  • A brain tumor is one of the most devastating diseases. Early detection of brain tumor is a life-saving act. Magnetic Resonance Imaging (MRI) is one of the main techniques to detect brain tumor for diagnosis and treatment. Although there are numerous methods for brain tumor segmentation, automatic and exact segmentation still confronted with some problems and remain one of the most challenging tasks in medical data processing.

    This paper presents a machine learning algorithm to classify MRI scans to be normal or abnormal using three techniques of classification which are Support Vector Machine (SVM), Linear Discriminant Analysis (LDA) and Artificial Neural network (ANN) , these techniques of classification are tested on a large database with accuracy of 93.06%, 97.45% and 98.9% respectively, then the detection of the brain tumor region from MRI abnormal scan images is performed using a hybrid method that is based on morphological operations , Filtering and Histogram Processing on images.

     

     

  • References

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    Mohammed Elsayed, M., Twakol Khalil, A., Omar Mohamed Diab, T., Shawky Selim S. Mohra, A., & ., . (2018). Hybrid method for brain tumor extraction and MRI scan classification. International Journal of Engineering & Technology, 7(4), 4769-4779. https://doi.org/10.14419/ijet.v7i4.27944