Soft Computing

, Volume 23, Issue 19, pp 9083–9096 | Cite as

K-Means clustering and neural network for object detecting and identifying abnormality of brain tumor

  • N. Arunkumar
  • Mazin Abed MohammedEmail author
  • Mohd Khanapi Abd Ghani
  • Dheyaa Ahmed Ibrahim
  • Enas Abdulhay
  • Gustavo Ramirez-Gonzalez
  • Victor Hugo C. de Albuquerque


Brain tumor diagnosis is a challenging and difficult process in view of the assortment of conceivable shapes, regions, and image intensities. The pathological detection and identification of brain tumor and comparison among normal and abnormal tissues need grouped scientific techniques for features extraction, displaying, and measurement of the disease images. Our study shows an improved automated brain tumor segmentation and identification approach using ANN from MR images without human mediation by applying the best attributes toward preparatory brain tumor case revelation. To obtain the exact district region of brain tumor from MR images, we propose a brain tumor segmentation technique that has three noteworthy improvement focuses. To begin with, K-means clustering will be utilized as a part of the principal organization in the process of improving the MR image to be marked in the districts regions in light of their gray scale. Second, ANN is utilized to choose the correct object in view of training phase. Third, texture feature of brain tumor area will be extracted to the division stage. With respect to the brain tumor identification, the grayscale features are utilized to analyze and diagnose the brain tumor to differentiate the benign and malignant cases. According to the study results demonstrated that: (1) enhancement adaptive strategy was utilized as post-processing in brain tumor identification; (2) identify and build an assessment foundation of automated segmentation and identification for brain tumor cases; (3) highlight the methods based on region growing method and K-means clustering technique to select the best region; and (4) evaluate the proficiency of the foreseen outcomes by comparing ANN and SVM segmentation outcomes, and brain tumor cases classification. The ANN approach classifier recorded accuracy of 94.07% with line assumption (brain tumor cases classification) and sensitivity of 90.09% and specificity of 96.78%.


Brain tumor Image segmentation Automatic segmentation Brain identification Artificial neural networks K-Means clustering Magnetic resonance images Machine learning methods 


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Conflict of interest

There is no conflict of interests.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Copyright information

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

Authors and Affiliations

  • N. Arunkumar
    • 1
  • Mazin Abed Mohammed
    • 2
    • 3
    Email author
  • Mohd Khanapi Abd Ghani
    • 2
  • Dheyaa Ahmed Ibrahim
    • 3
  • Enas Abdulhay
    • 4
  • Gustavo Ramirez-Gonzalez
    • 5
  • Victor Hugo C. de Albuquerque
    • 6
  1. 1.SASTRA UniversityThanjavurIndia
  2. 2.Biomedical Computing and Engineering Technologies (BIOCORE), Applied Research Group, Faculty of Information and Communication TechnologyUniversiti Teknikal Malaysia MelakaMelakaMalaysia
  3. 3.Planning and Follow Up Department, University HeadquarterUniversity of AnbarAnbarIraq
  4. 4.Department of Biomedical EngineeringJordan University of Science and TechnologyIrbidJordan
  5. 5.Department of TelematicsUniversity de CaucaCaucaColombia
  6. 6.Graduate Program in Applied InformaticsUniversity of FortalezaFortalezaBrazil

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