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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
Focus

Abstract

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%.

Keywords

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

Notes

Compliance with ethical standards

Conflict of interest

There is no conflict of interests.

Ethical approval

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

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  1. Abdel-Basset M, Fakhry AE, El-Henawy I, Qiu T, Sangaiah AK (2017) Feature and intensity based medical image registration using particle swarm optimization. J Med Syst 41(12):197CrossRefGoogle Scholar
  2. Abdulhay E, Mohammed MA, Ibrahim DA, Arunkumar N, Venkatraman V (2018) Computer aided solution for automatic segmenting and measurements of blood leucocytes using static microscope images. J Med Syst 42(4):58CrossRefGoogle Scholar
  3. Ahmad MS, Mohammed MA (2018) Evaluating the performance of three classification methods in diagnosis of Parkinson’s disease. In: Recent advances on soft computing and data mining: proceedings of the third international conference on soft computing and data mining (SCDM 2018), vol 700, Johor, Malaysia, 6–7 Feb 2018, Springer, p 43Google Scholar
  4. Ali Z, Hossain MS, Muhammad G, Sangaiah AK (2018) An intelligent healthcare system for detection and classification to discriminate vocal fold disorders. Future Gener Comput Syst 85:19–28CrossRefGoogle Scholar
  5. Benamrane N, Aribi A, Kraoula L (1993) Fuzzy neural networks and genetic algorithms for medical images interpretation. In: Geometric modeling and imaging–new trends, IEEE, pp 259–264Google Scholar
  6. Binder T, Süssner M, Moertl D, Strohmer T, Baumgartner H, Maurer G, Porenta G (1999) Artificial neural networks and spatial temporal contour linking for automated endocardial contour detection on echocardiograms: a novel approach to determine left ventricular contractile function. Ultrasound Med Biol 25(7):1069–1076CrossRefGoogle Scholar
  7. Cabria I, Gondra I (2017) MRI segmentation fusion for brain tumor detection. Inf Fusion 36:1–9CrossRefGoogle Scholar
  8. Castellanos R, Mitra S (2000) Segmentation of magnetic resonance images using a neuro-fuzzy algorithm. In: Proceedings 13th IEEE symposium on computer-based medical systems, 2000. CBMS 2000, IEEE, pp 207–212Google Scholar
  9. Corso JJ, Sharon E, Dube S, El-Saden S, Sinha U, Yuille A (2008) Efficient multilevel brain tumor segmentation with integrated bayesian model classification. IEEE Trans Med Imaging 27(5):629–640CrossRefGoogle Scholar
  10. Dubey RB, Hanmandlu M, Gupta SK (2009) Region growing for MRI brain tumor volume analysis. Indian J Sci Technol 2(9):26–31Google Scholar
  11. Gordillo N, Montseny E, Sobrevilla P (2013) State of the art survey on MRI brain tumor segmentation. Magn Reson Imaging 31(8):1426–1438CrossRefGoogle Scholar
  12. Hanning C, Yunlong Z, Kunyuan H (2011) Adaptive bacterial foraging optimization. Abstr Appl Anal 1:1–27MathSciNetCrossRefzbMATHGoogle Scholar
  13. Havaei M, Davy A, Warde-Farley D, Biard A, Courville A, Bengio Y, Pal C, Jodoin PM, Larochelle H (2017) Brain tumor segmentation with deep neural networks. Med Image Anal 35:18–31CrossRefGoogle Scholar
  14. Hong CM, Chen CM, Chen SY, Huang CY (2006) A novel and efficient neuro-fuzzy classifier for medical diagnosis. In: International joint conference on neural networks 2006 (IJCNN’06), IEEE, pp 735–741Google Scholar
  15. Huang KW, Zhao ZY, Gong Q, Zha J, Chen L, Yang R (2015) Nasopharyngeal carcinoma segmentation via HMRF-EM with maximum entropy. In: 2015 37th annual international conference of the IEEE engineering in medicine and biology society (EMBC), pp 2968–2972Google Scholar
  16. Jiang W, Yang X, Wu W, Liu K, Ahmad A, Sangaiah AK, Jeon G (2018) Medical images fusion by using weighted least squares filter and sparse representation. Comput Electr Eng 67:252–266CrossRefGoogle Scholar
  17. Juang LH, Wu MN (2010) MRI brain lesion image detection based on color-converted K-means clustering segmentation. Measurement 43(7):941–949CrossRefGoogle Scholar
  18. Ma R, Wang K, Qiu T, Sangaiah AK, Lin D, Liaqat HB (2017) Featurebased compositing memory networks for aspect-based sentiment classification in social internet of things. Future Gener Comput Syst.  https://doi.org/10.1016/j.future.2017.11.036 Google Scholar
  19. Mohammed MA, Ghani MKA, Hamed RI, Abdullah MK, Ibrahim DA (2017a) Automatic segmentation and automatic seed point selection of nasopharyngeal carcinoma from microscopy images using region growing based approach. J Comput Sci 20:61–69Google Scholar
  20. Mohammed MA, Ghani MKA, Hamed RI, Ibrahim DA (2017b) Review on Nasopharyngeal Carcinoma: concepts, methods of analysis, segmentation, classification, prediction and impact: a review of the research literature. J Comput Sci 21:283–298CrossRefGoogle Scholar
  21. Mohammed MA, Ghani MKA, Hamed RI, Ibrahim DA (2017c) Analysis of an electronic methods for nasopharyngeal carcinoma: prevalence, diagnosis, challenges and technologies. J Comput Sci 21:241–254CrossRefGoogle Scholar
  22. Mohammed MA, Ghani MKA, Hamed RI, Ibrahim DA, Abdullah MK (2017d) Artificial neural networks for automatic segmentation and identification of nasopharyngeal carcinoma. J Comput Sci 21:263–274CrossRefGoogle Scholar
  23. Mohammed MA, Ghani MKA, Arunkumar N, Hamed RI, Abdullah MK, Burhanuddin MA (2018a) A real time computer aided object detection of nasopharyngeal carcinoma using genetic algorithm and artificial neural network based on Haar feature fear. Future Gener Comput Syst 89:539–547CrossRefGoogle Scholar
  24. Mohammed MA, Abd Ghani MK, Arunkumar N et al (2018b) Decision support system for nasopharyngeal carcinoma discrimination from endoscopic images using artificial neural network. J Supercomput.  https://doi.org/10.1007/s11227-018-2587-z Google Scholar
  25. Mohammed MA et al (2018c) Neural network and multi-fractal dimension features for breast cancer classification from ultrasound images. Comput Electr Eng.  https://doi.org/10.1016/j.compeleceng.2018.01.033 Google Scholar
  26. Mostafa SA, Mustapha A, Mohammed MA, Ahmad MS, Mahmoud MA (2018) A fuzzy logic control in adjustable autonomy of a multi-agent system for an automated elderly movement monitoring application. Int J Med Inf 112:173–184CrossRefGoogle Scholar
  27. Mutlag AA, Ghani MKA, Arunkumar N, Mohamed MA, Mohd O (2019) Enabling technologies for fog computing in healthcare IoT systems. Future Gener Comput Syst 90:62–78CrossRefGoogle Scholar
  28. Oliva D, Hinojosa S, Cuevas E, Pajares G, Avalos O, Gálvez J (2017) Cross entropy based thresholding for magnetic resonance brain images using Crow Search algorithm. Expert Syst Appl 79:164–180CrossRefGoogle Scholar
  29. Oweis RJ, Sunna MJ (2005) A combined neuro-fuzzy approach for classifying image pixels in medical applications. J Electr Eng Bratisl 56(5/6):146Google Scholar
  30. Prince JL, Links JM (2006) Medical imaging signals and systems. Pearson Prentice Hall, Upper Saddle RiverGoogle Scholar
  31. Ramakrishnan T, Sankaragomathi B (2017) A professional estimate on the computed tomography brain tumor images using SVM-SMO for classification and MRG-GWO for segmentation. Pattern Recognit Lett 94:163–171CrossRefGoogle Scholar
  32. Samuel OW, Asogbon GM, Sangaiah AK, Fang P, Li G (2017) An integrated decision support system based on ANN and Fuzzy_AHP for heart failure risk prediction. Expert Syst Appl 68:163–172CrossRefGoogle Scholar
  33. Samuel OW, Zhou H, Li X, Wang H, Zhang H, Sangaiah AK, Li G (2018) Pattern recognition of electromyography signals based on novel time domain features for amputees’ limb motion classification. Comput Electr Eng 67:646–655CrossRefGoogle Scholar
  34. Sompong C, Wongthanavasu S (2017) An efficient brain tumor segmentation based on cellular automata and improved tumor-cut algorithm. Expert Syst Appl 72:231–244CrossRefGoogle Scholar
  35. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612CrossRefGoogle Scholar
  36. Yang AY, Wright J, Ma Y, Sastry SS (2008) Unsupervised segmentation of natural images via lossy data compression. Comput Vis Image Underst 110(2):212–225CrossRefGoogle Scholar
  37. Yu Q, Clausi DA (2008) IRGS: image segmentation using edge penalties and region growing. IEEE Trans Pattern Anal Mach Intell 30:2126–2139CrossRefGoogle Scholar

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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