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Brain tumor classification based on hybrid approach

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Abstract

Various computer systems have attracted more researchers’ attention to arrive at a qualitative diagnosis in a few times. Different brain tumor classification approaches are proposed due to lesion complexity. This complexity makes the early tumor diagnosis using magnetic resonance images (MRI) a hard step. However, the accuracy of these techniques requires a significant amelioration to meet the needs of real-world diagnostic situations. We aim to classify three brain tumor types in this paper. A new technique is suggested which provides excellent results and surpasses the previous schemes. The proposed scheme makes use of the normalization, dense speeded up robust features, and histogram of gradient approaches to ameliorate MRI quality and generate a discriminative feature set. We exploit support vector machine in the classification step. The suggested system is benchmarked on an important dataset. The accuracy achieved based on this scheme is 90.27%. This method surpassed the most recent system according to experimental results. The results were earned through a strict statistical analysis (k-fold cross-validation), which proves the reliability and robustness of the suggested method.

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Authors and Affiliations

  1. Laboratory of Electronics and Microelectronics, University of Monastir, Monastir, 5000, Tunisia

    Wadhah Ayadi

  2. Higher School of Sciences and Technology of Hammam Sousse, University of Sousse, Sousse, Tunisia

    Imen Charfi

  3. College of Computer Sciences and Engineering, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia

    Wajdi Elhamzi

  4. College of Computer Science, King Khalid University, Abha, Saudi Arabia

    Mohamed Atri

Authors
  1. Wadhah Ayadi
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  2. Imen Charfi
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  3. Wajdi Elhamzi
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  4. Mohamed Atri
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Correspondence to Wajdi Elhamzi.

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Ayadi, W., Charfi, I., Elhamzi, W. et al. Brain tumor classification based on hybrid approach. Vis Comput 38, 107–117 (2022). https://doi.org/10.1007/s00371-020-02005-1

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