Evolutionary Intelligence

, Volume 11, Issue 1–2, pp 19–30 | Cite as

Evolutionary intelligence for brain tumor recognition from MRI images: a critical study and review

  • K. Michael MaheshEmail author
  • J. Arokia Renjit
Special Issue


In medical image analysis, brain tumor recognition through medical resonance images (MRIs) is a challenging task because of the complex structure of the brain and high diversity in appearance of tumor tissues. Hence, the need for efficient and objective tumor recognition technique is increasing, for clinical acceptance as well as routine clinical application. Proper brain tumor recognition provides anatomical information of abnormal tissues in the brain, which helps the doctor in planning treatment. The literature presents various techniques for brain tumor recognition. This review article aims to provide a comprehensive survey of MRI based brain tumor recognition techniques based on evolutional intelligence and segmentation. Accordingly, various research papers related to brain tumor recognition are reviewed, and survey taxonomy is presented centered on segmentation and classification based tumor recognition techniques. Based on the review, the analysis is provided based on feature extraction techniques, image datasets, implementation tools, evaluation measures and results. Finally, we present various research issues which are useful for researchers to further research in brain tumor recognition techniques.


MRI Imaging modalities Brain tumor Classification Segmentation 



Medical resonance image


Computed tomography




Diffusion tensor imaging


Echo-planar imaging


World Health Organization


Cerebro spinal fluid


Proton density


Multiple sclerosis


Multi-population Cuckoo search strategy


Cuckoo search


Modified shuffled frog leaping


Fuzzy C-means


Artificial bee colony


Gray matter


White matter


Artificial neural network


Neural network


Self-organizing map


k-nearest neighbor


Support vector machine


Deep neural network


Fully convolutional networks


Random forest


Generalized autoregressive conditional heteroscedasticity


Fluid-attenuated inversion recovery


Extremely randomized trees




Glioblastoma multiforme


k-means and fuzzy c-means


k-means and particle swarm optimization


Gray level run length matrix


Discrete wavelet transformation


Principal component analysis


Medical image computing and computer assisted interventions



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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.St. Joseph College of EngineeringChennaiIndia
  2. 2.Department of CSEJeppiaar Engineering CollegeChennaiIndia

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