Brain tumor detection based on extreme learning

Abstract

Gliomas are dreadful and common type of brain tumor. Therefore, treatment planning is significant to increase the survival rate of gliomas patients. The large structural and spatial variation between tumors makes an automated detection more challenging. Brain magnetic resonance imaging is utilized for tumor evaluation on the basis of automated segmentation and classification methods. In this work, triangular fuzzy median filtering is applied for image enhancement that helps in accurate segmentation based on unsupervised fuzzy set method. Gabor features are extracted across each candidate’s lesions, and similar texture (ST) features are calculated. These ST features are supplied to extreme learning machine (ELM), and regression ELM leaves one out for tumor classification. The technique is evaluated on BRATS 2012, 2013, 2014 and 2015 challenging datasets as well as on 2013 Leader board. The proposed approach shows better results and less computational time.

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Correspondence to Mudassar Raza.

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Sharif, M., Amin, J., Raza, M. et al. Brain tumor detection based on extreme learning. Neural Comput & Applic 32, 15975–15987 (2020). https://doi.org/10.1007/s00521-019-04679-8

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Keywords

  • Fuzzy rules
  • Erosion
  • MRI
  • Dilation
  • Gabor filter
  • Gliomas