A Comprehensive Analysis of MRI Based Brain Tumor Segmentation Using Conventional and Deep Learning Methods

  • Hikmat Khan
  • Syed Farhan Alam Zaidi
  • Asad SafiEmail author
  • Shahab Ud Din
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1187)


Brain tumor segmentation plays an important role in clinical diagnosis for neurologists. Different imaging modalities have been used to diagnose and segment brain tumor. Among all modalities, MRI is preferred because of its non-invasive nature and better visualization of internal details of the brain. However, MRI also comes with certain challenges like random noise, various intensity levels, and in-homogeneity that makes detection and segmentation a difficult task. Manual segmentation is extremely laborious and time consuming for the physicians. Manual segmentation is also highly dependent on the physician’s domain knowledge and practical experience. Also, the physician may not be able to see details at the pixel level and may only notice the tumor if it is more prominent and obvious. Therefore, there is a need for brain tumor segmentation techniques that play major role in perfect visualization to assist the physician in identifying different tumor regions. In this paper, we present recent advancements and comprehensive analysis of MRI-based brain tumor segmentation techniques that used conventional machine learning and deep learning methods. We analyze different proposed conventional and state-of-the-art methods in chronological order using Dice similarity, specificity, and sensitivity as performance measures.


Brain tumor Segmentation Machine Learning Deep Learning Dice Similarity 2D and 3D convolutional ANN 


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Hikmat Khan
    • 1
  • Syed Farhan Alam Zaidi
    • 2
  • Asad Safi
    • 3
    Email author
  • Shahab Ud Din
    • 3
  1. 1.Department of Computer ScienceCOMSATS UniversityIslamabadPakistan
  2. 2.Department of Computer Science and EngineeringChung-Ang UniversitySeoulSouth Korea
  3. 3.Faculty of Computer Information Science (CIS)Higher Colleges of Technology, Sharjah Women’s CampusSharjahUAE

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