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Gaussian hybrid fuzzy clustering and radial basis neural network for automatic brain tumor classification in MRI images

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Abstract

Magnetic Resonance Imaging (MRI) is an emerging research area, employed extensively in radiology for the diagnosis of various neurological diseases. Here, as the classification of MRI gains significant importance for the tumor diagnosis, various techniques are developed in the literature for the automatic classification of MRI images. In this paper, a clustering scheme, namely gaussian hybrid fuzzy clustering (GHFC) is developed by hybridizing the fuzzy c-means (FCM) clustering and sparse FCM along with the Gaussian function for the segmentation purpose. After segmenting the image, the suitable features are extracted from the image and given to the Exponential cuckoo based Radial Basis Neural Network (Exponential cuckoo based RBNN) classifier. The features serve as the training information for the Exponential cuckoo based RBNN classifier, and it finally detects the training class. Simulation of the proposed work is done using BRATS and SIMBRATS databases, and the results are compared with several state-of-art techniques. Simulation results depict that the proposed GHFC along with the RBNN classifier achieved improved accuracy and mean squared error results with the values of 0.8952, and 0.0074, respectively, for the BRATS dataset and 0.8719 and 0.0036, for the SIMBRATS dataset.

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References

  1. Iqbal S, Khan MUG, Saba T, Rehman A (2017) Computer-assisted brain tumor type discrimination using magnetic resonance imaging features. Biomed Eng Lett 8(1):5–28

    Article  Google Scholar 

  2. Shree NV, Kumar TNR (2018) Identification and classification of brain tumor MRI images with feature extraction using DWT and probabilistic neural network. Brain Inf 5(1):23–30

    Article  Google Scholar 

  3. Portela NM, Cavalcanti GDC, Ing T (2014) Semi-supervised clustering for MR brain image segmentation. Expert Syst Appl 41(4):1492–1497

    Article  Google Scholar 

  4. 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 Recogn Lett 94:163–171

    Article  Google Scholar 

  5. Angulakshmi M, Priya GL (2018) Brain tumour segmentation from MRI using superpixels based spectral clustering. J King Saud Univ-Comput Inf Sci

  6. Kanmani P, Marikkannu P (2018) MRI brain images classification: a multi-level threshold based region optimization technique. J Med Syst 42(4):62

    Article  Google Scholar 

  7. Aswathy SU, Devadhas GG, Kumar SS (2019) Brain tumor detection and segmentation using a wrapper based genetic algorithm for optimized feature set. Clust Comput 22(1):13369–13380

    Article  Google Scholar 

  8. Bahadure NB, Ray AK, Thethi HP (2018) Comparative approach of MRI-based brain tumor segmentation and classification using genetic algorithm. J Digit Imaging 31(4):477–489

    Article  Google Scholar 

  9. Rajesh T, Malar RSM, Geetha MR (2019) Brain tumor detection using optimisation classification based on rough set theory. Clust Comput 22(1):13853–13859

    Article  Google Scholar 

  10. 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 

  11. Hussain S, Anwar SM, Majid M (2018) Segmentation of glioma tumors in brain using deep convolutional neural network. Neurocomputing 282:248–261

    Article  Google Scholar 

  12. Pinto A, Pereira S, Rasteiro D, Silva CA (2018) Hierarchical brain tumour segmentation using extremely randomized trees. Pattern Recogn 82:105–117

    Article  Google Scholar 

  13. Ilunga-Mbuyamba E, Avina-Cervantes JG, Cepeda-Negrete J, Ibarra-Manzano MA, Chalopin C (2017) Automatic selection of localized region-based active contour models using image content analysis applied to brain tumor segmentation. Comput Biol Med 91:69–79

    Article  Google Scholar 

  14. Sathish P, Elango NM (2019) Exponential cuckoo search algorithm to Radial Basis Neural Network for automatic classification in MRI images. Comput Methods Biomech Biomed Eng: Imaging Vis 7(3):273–285

    Google Scholar 

  15. Chang X, Wang Q, Liu Y, Wang Y (2016) Sparse regularization in fuzzy c-means for high-dimensional data clustering. IEEE Trans Cybern 47(7):1–12

    Article  Google Scholar 

  16. Chakraborti T, McCane B, Mills S, Pal U (2017) LOOP descriptor: local optimal oriented pattern. IEEE Signal Process Lett 25(5):1–5

    Google Scholar 

  17. Ortiz A, Gorriz JM, Ramirez J, Salas-Gonzalez D (2014) Improving MR brain image segmentation using self-organising maps and entropy-gradient clustering. Inf Sci 262:117–136

    Article  Google Scholar 

  18. Nabizadeh N, Kubat M (2015) Brain tumors detection and segmentation in MR images: gabor wavelet vs. statistical features. Comput Electr Eng 45:286–301

    Article  Google Scholar 

  19. Vishnuvarthanan G, Rajasekaran MP, Subbaraj P, Vishnuvarthanan A (2016) An unsupervised learning method with a clustering approach for tumor identification and tissue segmentation in magnetic resonance brain images. Appl Soft Comput 38:190–212

    Article  Google Scholar 

  20. Zhao X, Wu Y, Song G, Li Z, Zhang Y, Fan Y (2018) A deep learning model integrating FCNNs and CRFs for brain tumor segmentation. Med Image Anal 43:98–111

    Article  Google Scholar 

  21. Ural B (2018) A computer-based brain tumor detection approach with advanced image processing and probabilistic neural network methods. J Med Biol Eng 38(22):867–879

    Article  Google Scholar 

  22. Amin J, Sharif M, Yasmin M, Fernandes SL (2017) A distinctive approach in brain tumor detection and classification using MRI. Pattern Recogn Lett

  23. Kaur T, Saini BS, Gupta S (2018) A novel feature selection method for brain tumor MR image classification based on the fisher criterion and parameter-free bat optimization. Neural Comput Appl 29(8):193–206

    Article  Google Scholar 

  24. Seetha J, Selvakumar Raja S (2018) Brain tumor classification using convolutional neural networks. Biomed Pharmacol J 11(3):1457–1461

    Article  Google Scholar 

  25. Busa S, Vangala NS, Grandhe P, Balaji V (2018) Automatic brain tumor detection using fast fuzzy C-means algorithm. Innov Comput Sci Eng 32:249–254

    Article  Google Scholar 

  26. Usman K, Rajpoot K (2017) Brain tumor classification from multi-modality MRI using wavelets and machine learning. Pattern Anal Appl 20(3):871–881

    Article  MathSciNet  Google Scholar 

  27. Soltaninejad M, Yang G, Lambrou T, Allinson N, Jones TL, Barrick TR, Howe FA, Ye X (2018) Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels. Comput Methods Programs Biomed 157:69–84

    Article  Google Scholar 

  28. Bezdek JC, Ehrlich R, Full W (1984) FCM: the fuzzy c-means clustering algorithm. Comput Geosci 10(2–3):191–203

    Article  Google Scholar 

  29. Jolliffe I (2011) Principal component analysis. In: Lovric M (ed) International encyclopedia of statistical science. Springer, Berlin, pp 1094–1096

    Chapter  Google Scholar 

  30. Gandomi AH, Yang XS, Alavi AH (2013) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput 29(1):17–35

    Article  Google Scholar 

  31. Holt CC (2004) Forecasting seasonals and trends by exponentially weighted moving averages. Int J Forecast 20(1):5–10

    Article  Google Scholar 

  32. Menze BH et al (2014) The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans Med Imaging 34(10):1993–2024

    Article  Google Scholar 

  33. BRATS database, http://www2.imm.dtu.dk/projects/BRATS2012/data.html. Accessed July 2018

  34. Zulpe N, Pawar V (2012) GLCM textural features for brain tumor classification. Int J Comput Sci Iss (IJCSI) 9(3):354

    Google Scholar 

  35. Sachdeva J, Kumar V, Gupta I, Khandelwal N, Ahuja CK (2011) Multiclass brain tumor classification using GA-SVM. Developments in E-systems Engineering, Dubai, United Arab Emirates

Download references

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Authors and Affiliations

  1. Department of Computer Science, Indian Academy Degree College-Autonomous, Bangalore, Karnataka, India

    P. Sathish

  2. School of Information Technology and Engineering, VIT University, Vellore, Tamilnadu, India

    N. M. Elango

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  1. P. Sathish
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  2. N. M. Elango
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Correspondence to P. Sathish.

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Sathish, P., Elango, N.M. Gaussian hybrid fuzzy clustering and radial basis neural network for automatic brain tumor classification in MRI images. Evol. Intel. 15, 1359–1377 (2022). https://doi.org/10.1007/s12065-020-00433-5

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  • DOI: https://doi.org/10.1007/s12065-020-00433-5

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