Skip to main content
SpringerLink
Log in

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

  • Focus
  • Published:
Soft Computing Aims and scope Submit manuscript

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%.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  • 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):197

    Article  Google Scholar 

  • 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):58

    Article  Google Scholar 

  • 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 43

  • 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 85:19–28

    Article  Google Scholar 

  • 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 259–264

  • 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):1069–1076

    Article  Google Scholar 

  • Cabria I, Gondra I (2017) MRI segmentation fusion for brain tumor detection. Inf Fusion 36:1–9

    Article  Google Scholar 

  • 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 207–212

  • 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):629–640

    Article  Google Scholar 

  • Dubey RB, Hanmandlu M, Gupta SK (2009) Region growing for MRI brain tumor volume analysis. Indian J Sci Technol 2(9):26–31

    Google Scholar 

  • Gordillo N, Montseny E, Sobrevilla P (2013) State of the art survey on MRI brain tumor segmentation. Magn Reson Imaging 31(8):1426–1438

    Article  Google Scholar 

  • Hanning C, Yunlong Z, Kunyuan H (2011) Adaptive bacterial foraging optimization. Abstr Appl Anal 1:1–27

    Article  MathSciNet  MATH  Google Scholar 

  • 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 35:18–31

    Article  Google Scholar 

  • 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 735–741

  • 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 2968–2972

  • 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 67:252–266

    Article  Google Scholar 

  • Juang LH, Wu MN (2010) MRI brain lesion image detection based on color-converted K-means clustering segmentation. Measurement 43(7):941–949

    Article  Google Scholar 

  • 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 

  • 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 20:61–69

    Google Scholar 

  • 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 21:283–298

    Article  Google Scholar 

  • 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 21:241–254

    Article  Google Scholar 

  • 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 21:263–274

    Article  Google Scholar 

  • 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 89:539–547

    Article  Google Scholar 

  • 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 

  • 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 

  • 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 112:173–184

    Article  Google Scholar 

  • Mutlag AA, Ghani MKA, Arunkumar N, Mohamed MA, Mohd O (2019) Enabling technologies for fog computing in healthcare IoT systems. Future Gener Comput Syst 90:62–78

    Article  Google Scholar 

  • 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 79:164–180

    Article  Google Scholar 

  • Oweis RJ, Sunna MJ (2005) A combined neuro-fuzzy approach for classifying image pixels in medical applications. J Electr Eng Bratisl 56(5/6):146

    Google Scholar 

  • Prince JL, Links JM (2006) Medical imaging signals and systems. Pearson Prentice Hall, Upper Saddle River

    Google Scholar 

  • 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 94:163–171

    Article  Google Scholar 

  • 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 68:163–172

    Article  Google Scholar 

  • 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 67:646–655

    Article  Google Scholar 

  • Sompong C, Wongthanavasu S (2017) An efficient brain tumor segmentation based on cellular automata and improved tumor-cut algorithm. Expert Syst Appl 72:231–244

    Article  Google Scholar 

  • Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612

    Article  Google Scholar 

  • Yang AY, Wright J, Ma Y, Sastry SS (2008) Unsupervised segmentation of natural images via lossy data compression. Comput Vis Image Underst 110(2):212–225

    Article  Google Scholar 

  • Yu Q, Clausi DA (2008) IRGS: image segmentation using edge penalties and region growing. IEEE Trans Pattern Anal Mach Intell 30:2126–2139

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. SASTRA University, Thanjavur, India

    N. Arunkumar

  2. Biomedical Computing and Engineering Technologies (BIOCORE), Applied Research Group, Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia

    Mazin Abed Mohammed & Mohd Khanapi Abd Ghani

  3. Planning and Follow Up Department, University Headquarter, University of Anbar, Anbar, Iraq

    Mazin Abed Mohammed & Dheyaa Ahmed Ibrahim

  4. Department of Biomedical Engineering, Jordan University of Science and Technology, Irbid, Jordan

    Enas Abdulhay

  5. Department of Telematics, University de Cauca, Cauca, Colombia

    Gustavo Ramirez-Gonzalez

  6. Graduate Program in Applied Informatics, University of Fortaleza, Fortaleza, CE, Brazil

    Victor Hugo C. de Albuquerque

Authors
  1. N. Arunkumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mazin Abed Mohammed
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohd Khanapi Abd Ghani
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dheyaa Ahmed Ibrahim
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Enas Abdulhay
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Gustavo Ramirez-Gonzalez
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Victor Hugo C. de Albuquerque
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mazin Abed Mohammed.

Ethics declarations

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.

Additional information

Communicated by A. K. Sangaiah, H. Pham, M.-Y. Chen, H. Lu, F. Mercaldo.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Arunkumar, N., Mohammed, M.A., Abd Ghani, M.K. et al. K-Means clustering and neural network for object detecting and identifying abnormality of brain tumor. Soft Comput 23, 9083–9096 (2019). https://doi.org/10.1007/s00500-018-3618-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-018-3618-7

Keywords

Search

Navigation