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Brain tumor segmentation and classification in MRI using SVM and its variants: a survey

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Abstract

The process of separation of brain tumor from normal brain tissues is Brain tumor segmentation. Segmentation of tumor from the MR images is a very challenging task as brain tumors are of different shapes and sizes. There are multiple phases to achieve the segmentation and the phases are pre-processing, segmentation, feature extraction, feature reduction, and classification of the tumor into benign and malignant. In this paper, Otsu thresholding is used in segmentation phase, Discrete Wavelet Transform (DWT) in feature extraction phase, Principal Component Analysis (PCA) in feature reduction phase and Support Vector Machine (SVM), Least Squared-Support Vector Machine (LS-SVM), Proximal Support Vector Machine (PSVM) and Twin Support Vector Machine (TWSVM) in the classification phase. We have compared the performances of all these classifiers, where TWSVM outperformed all other classifiers with 100% accuracy.

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  1. MNNIT, Allahabad, Prayagraj, India

    Sonia Vadhnani

  2. IIIT, Allahabad, Prayagraj, India

    Navjot Singh

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  1. Sonia Vadhnani
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Vadhnani, S., Singh, N. Brain tumor segmentation and classification in MRI using SVM and its variants: a survey. Multimed Tools Appl 81, 31631–31656 (2022). https://doi.org/10.1007/s11042-022-12240-4

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