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RETRACTED ARTICLE: Brain Tumor Segmentation Using Deep Learning and Fuzzy K-Means Clustering for Magnetic Resonance Images

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This article was retracted on 04 April 2024

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Abstract

The primary objective of this paper is to develop a methodology for brain tumor segmentation. Nowadays, brain tumor recognition and fragmentation is one among the pivotal procedure in surgical and medication planning arrangements. It is difficult to segment the tumor area from MRI images due to inaccessibility of edge and appropriately visible boundaries. In this paper, a combination of Artificial Neural Network and Fuzzy K-means algorithm has been presented to segment the tumor locale. It contains four phases, (1) Noise evacuation (2) Attribute extraction and selection (3) Classification and (4) Segmentation. Initially, the procured image is denoised utilizing wiener filter, and then the significant GLCM attributes are extricated from the images. Then Deep Learning based classification has been performed to classify the abnormal images from the normal images. Finally, it is processed through the Fuzzy K-Means algorithm to segment the tumor region separately. This proposed segmentation approach has been verified on BRATS dataset and produces the accuracy of 94%, sensitivity of 98% specificity of 99%, Jaccard index of 96%. The overall accuracy of this proposed technique has been improved by 8% when compared with K-Nearest Neighbor methodology.

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References

  1. Osareh A, Shadgar B (2011) A computer aided diagnosis system for breast cancer. IJCSI Int J Comput Sci 8(2):535–545

    Google Scholar 

  2. Sapra P, Singh R, Khurana S (2013) Brain tumor detection using neural network. Int J Sci Mod Eng (IJISME) 1(9):2319–6386

    Google Scholar 

  3. Dudoit S, Fridlyand J, Speed TP (2002) Comparison of discrimination methods for the classification of tumors using gene expression data. J Am Stat Assoc 97(457):77–87

    Article  MathSciNet  Google Scholar 

  4. Li L, Darden TA, Weinberg CR, Levine AJ, Pedersen LG (2001) Gene, assessment and sample classification for Gene expression data using a genetic algorithm and k-nearest neighbor method. Comb Chem High Throughput Screen 4(8):727–739

    Article  Google Scholar 

  5. Khan J, Wei JS, Ringner M et al (2001) Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nat Med 7(673):679

    Google Scholar 

  6. Jong K, Mary J, Cornuejols A, Marchiori E, Sebag M (2004) Ensemble feature ranking. In: Proceedings of European conference on machine learning and principles and practice of knowledge discovery in databases

  7. Acır N, Ozdamar O, Guzelis C (2006) Automatic classification of auditory brainstem responses using SVM-based feature selection algorithm for threshold detection. Eng Appl Artif Intell 19:209–218

    Article  Google Scholar 

  8. Statnikov A, Aliferis CF, Tsamardinos I, Hardin D, Levy S (2005) A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis. Bioinformatics 21(5):631–643

    Article  Google Scholar 

  9. Popuri K, Cobzas D, Murtha A, Jägersand M (2012) 3D variational brain tumor segmentation using Dirichlet priors on a clustered feature set. Int J Comput Assis Radiol Surg 7(4):493–506

    Article  Google Scholar 

  10. Patil S, Udupi V (2012) Preprocessing to be considered for MR and CT images containing tumors. IOSR J Electr Electron Eng 1(4):54–57

    Article  Google Scholar 

  11. Wu W, Chen AY, Zhao L, Corso JJ (2014) Brain tumor detection and segmentation in a CRF (conditional random felds) framework with pixel-pairwise afnity and superpixel-level features. Int J Comput Assis Radiol Surg 9(2):241–253

    Article  Google Scholar 

  12. Chaddad A (2015) Automated feature extraction in brain tumor by magnetic resonance imaging using gaussian mixture models. Int J Biomed Imaging 2015:868031. https://doi.org/10.1155/2015/868031

    Article  Google Scholar 

  13. Damodharan S, Raghavan D (2015) Combining tissue segmentation and neural network for brain tumor detection. IAJIT 12:1

    Google Scholar 

  14. Abbasi S, Tajeripour F (2017) Detection of brain tumor in 3D MRI images using local binary patterns and histogram orientation gradient. Neurocomputing 219:526–535

    Article  Google Scholar 

  15. Ariyo O, Zhi-guang Q, Tian L (2017) Brain MR segmentation using a fusion of K-means and spatial fuzzy C-means. In: 2017 international conference on computer science and application engineering (CSAE 2017), pp 863–873

  16. El Abbadi NK, Kadhim NE (2017) Brain cancer classifcation based on features and artifcial neural network. Brain 6:1

    Google Scholar 

  17. Sharif M, Tanvir U, Munir EU, Khan MA, Yasmin M (2018) Brain tumor segmentation and classification by improved binomial thresholding and multi-features selection. J Ambient Intell Humanized Comput. https://doi.org/10.1007/s12652-018-1075-x

    Article  Google Scholar 

  18. Shubhangi N, Dudhane A, Murla S, Naidu S (2020) RescueNet: an unpaired GAN for brain tumor segmentation. Biomed Signal Process Control 55:101641

    Article  Google Scholar 

  19. Saba T, Mohamed AS, Affendi M, Amin J, Sharif M (2020) Brain tumor detection using fusion of hand crafted and deep learning features. Cognit Syst Res 59:221–230

    Article  Google Scholar 

  20. Amin J, Sharif M, Gul N, Yasmin M, AliShad S (2020) Brain tumor classification based on DWT fusion of MRI sequences using convolutional neural network. Pattern Recogn Lett 129:115–122

    Article  Google Scholar 

  21. Deng W, Shi Q, Wang M, Zheng B, Ning N (2020) Deep learning-based HCNN and CRF-RRNN model for brain tumor segmentation. IEEE Access 8:26665–26675

    Article  Google Scholar 

  22. Zhou C, Ding C, Wang X, Lu Z, Tao D (2020) One-pass multi-task networks with cross-task guided attention for brain tumor segmentation. IEEE Trans Image Process 29:4516–4529

    Article  Google Scholar 

  23. Emami H, Derakhshan F (2015) Integrating fuzzy K-means, particle swarm optimization, and imperialist competitive algorithm for data clustering. Arab J Sci Eng 40(12):3545–3554

    Article  Google Scholar 

  24. Chang C-T, Lai JZC, Jeng M-D (2011) A fuzzy K-means clustering algorithm using cluster center displacement. J Inf Sci Eng 27:995–1009

    MathSciNet  Google Scholar 

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Correspondence to P. Supraja.

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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s11063-024-11601-4

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Pitchai, R., Supraja, P., Victoria, A.H. et al. RETRACTED ARTICLE: Brain Tumor Segmentation Using Deep Learning and Fuzzy K-Means Clustering for Magnetic Resonance Images. Neural Process Lett 53, 2519–2532 (2021). https://doi.org/10.1007/s11063-020-10326-4

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  • DOI: https://doi.org/10.1007/s11063-020-10326-4

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