Abstract
In recent times, the detection of brain tumours has become more common. Generally, a brain tumour is an abnormal mass of tissue where the cells grow uncontrollably and are apparently unregulated by the mechanisms that control cells. A number of techniques have been developed thus far; however, the time needed in a detecting brain tumour is still a challenge in the field of image processing. This article proposes a new accurate detection model. The model includes certain processes such as preprocessing, segmentation, feature extraction and classification. Particularly, two extreme processes such as contrast enhancement and skull stripping are processed under the initial phase. In the segmentation process, we used the fuzzy means clustering (FCM) algorithm. Both the grey co-occurrence matrix (GLCM) as well as the grey-level run-length matrix (GRLM) features were extracted in the feature extraction phase. Moreover, this paper uses a deep belief network (DBN) for classification. The optimized DBN concept is used here, for which grey wolf optimisation (GWO) is used. The proposed model is termed the GW-DBN model. The proposed model compares its performance over other conventional methods in terms of accuracy, specificity, sensitivity, precision, negative predictive value (NPV), the F1Score and Matthews correlation coefficient (MCC), false negative rate (FNR), false positive rate (FPR) and false discovery rate (FDR), and proves the superiority of the proposed work.
Author Statement
Research funding: Authors state no funding involved.
Conflict of interest: Authors state no conflict of interest.
Informed consent: Informed consent is not applicable.
Ethical approval: The conducted research is not related to either human or animals use.
References
[1] Sherifi I, Senja E. Internet usage on mobile devices and their impact on evolution of informative websites in Albania. Euro J Business 2018;3:37–43.Search in Google Scholar
[2] Amin J, Sharif M, Yasmin M, Fernandes SL. A distinctive approach to brain tumor detection and classification using MRI. Pattern Recognit Lett 2017;10:116–56.10.1016/j.patrec.2017.10.036Search in Google Scholar
[3] Zhao X, Wu Y, Song G, Li Z, Zhang Y, Fan Y. A deep learning model integrating FCNNs and CRFs for brain tumor segmentation. Med Image Anal 2018;43:98–111.10.1016/j.media.2017.10.002Search in Google Scholar PubMed PubMed Central
[4] Ramakrishnan T, Sankaragomathi B. A professional estimate on the computed tomography brain tumor images using SVM-SMO for classification and MRG-GWO for segmentation. Pattern Recognit Lett 2017;94:163–71.10.1016/j.patrec.2017.03.026Search in Google Scholar
[5] Huang M, Yang W, Wu Y, Jiang J, Chen W, Feng Q. Brain tumor segmentation based on local independent projection-based classification. IEEE Trans Biomed Eng 2014;61:2633–45.10.1109/TBME.2014.2325410Search in Google Scholar PubMed
[6] Gholami B, Norton I, Eberlin LS, Agar NYR. A statistical modeling approach for tumor-type identification in surgical neuropathology using tissue mass spectrometry imaging. IEEE J Biomed Health Inform 2013;17:734–44.10.1109/JBHI.2013.2250983Search in Google Scholar PubMed
[7] Zhu Y, Yan Z. Computerized tumor boundary detection using a Hopfield neural network. IEEE Trans Med Imaging 1997;16:55–67.10.1109/42.552055Search in Google Scholar PubMed
[8] Islam A, Reza SMS, Iftekharuddin KM. Multifractal texture estimation for detection and segmentation of brain tumors. IEEE Trans Biomed Eng 2013;60:3204–15.10.1109/TBME.2013.2271383Search in Google Scholar PubMed PubMed Central
[9] Mbuyamba EI, Cervantes JGA, Negrete JC, Manzano MAI, Chalopin C. Automatic selection of localized region-based active contour models using image content analysis applied to brain tumor segmentation. Comput Biol Med 2017;91:69–79.10.1016/j.compbiomed.2017.10.003Search in Google Scholar PubMed
[10] Corso JJ, Sharon E, Dube S, El-Saden S, Sinha U, Yuille A. Efficient multilevel brain tumor segmentation with integrated Bayesian model classification. IEEE Trans Med Imaging 2008;27:629–40.10.1109/TMI.2007.912817Search in Google Scholar PubMed
[11] Hamamci A, Kucuk N, Karaman K, Engin K, Unal G. Tumor-cut: segmentation of brain tumors on contrast enhanced MR images for radiosurgery applications. IEEE Trans Med Imaging 2012;31:790–804.10.1109/TMI.2011.2181857Search in Google Scholar PubMed
[12] Iftekharuddin KM, Zheng J, Islam MA, Ogg RJ. Fractal-based brain tumor detection in multimodal MRI. Appl Math Comput 2009;2017:23–41.10.1016/j.amc.2007.10.063Search in Google Scholar
[13] Shasidhar M, Raja VS, Kumar BV. MRI Brain image segmentation using modified fuzzy C-means clustering algorithm. 2011 International Conference on Communication Systems and Network Technologies, Katra, Jammu; 2011:473–8.10.1109/CSNT.2011.102Search in Google Scholar
[14] Demirhan A, Törü M, Güler İ. Segmentation of tumor and edema along with healthy tissues of brain using wavelets and neural networks. IEEE J Biomed Health 2015;19:1451–8.10.1109/JBHI.2014.2360515Search in Google Scholar PubMed
[15] Nanthagopal AP, Sukanesh R. Wavelet statistical texture features-based segmentation and classification of brain computed tomography images. IET Image Process 2013;7:25–32.10.1049/iet-ipr.2012.0073Search in Google Scholar
[16] Wagh AM, Todmal SR. Eyelids, eyelashes detection algorithm and Hough transform method for noise removal in iris recognition. Int J Comput Appl 2015;112:28–31.Search in Google Scholar
[17] Rani N, Vashisth S. Brain tumor detection and classification with feed forward back-prop neural network. Int J Comput Appl 2016;146:1–6.10.5120/ijca2016910738Search in Google Scholar
[18] Vijay V, Kavitha AR, Rebecca SR. Automated brain tumor segmentation and detection in MRI using enhanced Darwinian particle swarm optimization (EDPSO). Comput Sci 2016;92:475–80.10.1016/j.procs.2016.07.370Search in Google Scholar
[19] Abbasi S, Tajeripour F. Detection of brain tumor in 3D MRI images using local binary patterns and histogram orientation gradient. Neurocomputing 2017;219:526–35.10.1016/j.neucom.2016.09.051Search in Google Scholar
[20] Chandra GR, Rao KRH. Tumor detection in brain using genetic algorithm. Comput Sci 2016;79:449–57.10.1016/j.procs.2016.03.058Search in Google Scholar
[21] Joshi DM, Rana NK, Misra VM. Classification of brain cancer using artificial neural network. 2nd International Conference on Electronic Computer Technology, Kuala Lumpur; 2010:112–6.10.1109/ICECTECH.2010.5479975Search in Google Scholar
[22] Alsam A, Farup I, Rivertz HJ. Iterative sharpening for image contrast enhancement. Colour Visual Computing Symp 2015:1–4.10.1109/CVCS.2015.7274891Search in Google Scholar
[23] Jui SL, Zhang S, Xiong W, Yu F, Fu M, Wang D, et al. Brain MRI tumor segmentation with 3D intracranial structure deformation features. IEEE Intell Syst 2016;31:66–76.10.1109/MIS.2015.93Search in Google Scholar
[24] Shanthakumar P, Ganeshkumar P. Performance analysis of classifier for brain tumor detection and diagnosis. Comput Electr Eng 2015;45:302–11.10.1016/j.compeleceng.2015.05.011Search in Google Scholar
[25] Sudharani K, Sarma TC, Prasad KS. Advanced morphological technique for automatic brain tumor detection and evaluation of statistical parameters. Technology 2016;24:1374–87.10.1016/j.protcy.2016.05.153Search in Google Scholar
[26] Kaur R, Kaur S. Comparison of contrast enhancement techniques for medical image. 2016 Conference on Emerging Devices and Smart Systems (ICEDSS), Namakkal; 2016:155–9.10.1109/ICEDSS.2016.7587782Search in Google Scholar
[27] Singh G, Jain VK, Singh A. Adaptive network architecture and firefly algorithm for biogas heating model aided by photovoltaic thermal greenhouse system. J Energ Environ 2018:1–25.10.1177/0958305X18768819Search in Google Scholar
[28] Sable AH, Jondhale KC. Modified double bilateral filter for sharpness enhancement and noise removal. 2010 International Conference on Advances in Computer Engineering, Bangalore; 2010:295–7.10.1109/ACE.2010.76Search in Google Scholar
[29] Congedo G, Dimauro G, Impedovo S, Pirlo G. Segmentation of numeric strings. Proceedings of 3rd International Conference on Document Analysis and Recognition 1995;2:1038–41.10.1109/ICDAR.1995.602080Search in Google Scholar
[30] Di Lecce V, Dimauro G, Guerriero A, Impedovo S, Pirlo G, Salzo A, et al. Selection of reference signatures for automatic signature verification. Proceedings of the Fifth International Conference on Document Analysis and Recognition, ICDAR’99 (Cat. No. PR00318), Bangalore, India; 1999:597–600.10.1109/ICDAR.1999.791858Search in Google Scholar
[31] Szwarc P, Kawa J, Rudzki M, Pietka E. Automatic brain tumour detection and neovasculature assessment with multiseries MRI analysis. Comput Med Imaging Graph 2015;46:178–90.10.1016/j.compmedimag.2015.06.002Search in Google Scholar PubMed
[32] Anitha V, Murugavalli S. Brain tumour classification using two-tier classifier with adaptive segmentation technique. IET Comput Vis 2016;10:9–17.10.1049/iet-cvi.2014.0193Search in Google Scholar
[33] Ain Q, Jaffar MA, Choi T. Fuzzy anisotropic diffusion based segmentation and texture basedensemble classification of brain tumor. Appl Soft Comput 2014;21:330–40.10.1016/j.asoc.2014.03.019Search in Google Scholar
[34] Kaur T, Saini BS, Gupta S. Quantitative metric for MR brain tumour grade classification using sample space density measure of analytic intrinsic mode function representation. IET Image Process 2017;11:620–32.10.1049/iet-ipr.2016.1103Search in Google Scholar
[35] Zhang Y, Wang S, Sun P, Phillips P. Pathological brain detection based on wavelet entropy and Hu moment invariants. Biomed Mater Eng 2015;26:S1283–90.10.3233/BME-151426Search in Google Scholar PubMed
[36] Wang S, Zhang Y, Yang X, Sun P, Dong Z, Liu A, et al. Pathological brain detection by a novel image feature–fractional Fourier entropy. Entropy 2015;17:8278–96.10.3390/e17127877Search in Google Scholar
[37] Wang S, Lu S, Dong Z, Yang J, Yang M, Zhang Y. Dual-tree complex wavelet transform and twin support vector machine for pathological brain detection. Appl Sci 2016;6:1–18.10.3390/app6060169Search in Google Scholar
[38] Isa IS, Sulaiman SN, Mustapha M, Khairiah N, Karim A. Automatic contrast enhancement of brain MR images using average intensity replacement based on adaptive histogram equalization (AIR-AHE). Biocybern Biomed Eng 2017;37:24–34.10.1016/j.bbe.2016.12.003Search in Google Scholar
[39] Roy S, Maji P. A simple skull stripping algorithm for brain MRI. 2015 Eighth International Conference on Advances in Pattern Recognition (ICAPR), Kolkata; 2015:1–6.10.1109/ICAPR.2015.7050671Search in Google Scholar
[40] Mohammed HR, Alnoamani HH, Zahraa AA. Improved FUZZY C-Mean algorithm for image segmentation. Int J Adv Res Artif Intel 2016;5:7–10.Search in Google Scholar
[41] Harshavardhan A, Babu S, Venugopal T. Analysis of feature extraction methods for the classification of brain tumor detection. Int J Pure Appl Math 2017;117:147–55.Search in Google Scholar
[42] Wang HZ, Wang GB, Li GQ, Peng JC, Liu YT. Deep belief network based deterministic and probabilistic wind speed forecasting approach. Appl Energ 2016;182:80–93.10.1016/j.apenergy.2016.08.108Search in Google Scholar
[43] Mirjalili S, Mirjalili SM, Lewis A. Grey wolf optimizer. Adv Eng Softw 2014;69:46–61.10.1016/j.advengsoft.2013.12.007Search in Google Scholar
[44] Muralidharan V, Sugumaran V. A comparative study of Naïve Bayes classifier and Bayes net classifier for fault diagnosis of monoblock centrifugal pump using wavelet analysis. Appl Soft Comput 2012;12:2023–9.10.1016/j.asoc.2012.03.021Search in Google Scholar
[45] Meyera D, Leisch F, Hornik K. The support vector machine under test. Neurocomputing 2003;55:169–86.10.1016/S0925-2312(03)00431-4Search in Google Scholar
[46] Mohan Y, Chee SS, Xin DKP, Foong LP. Artificial neural network for classification of depressive and normal in EEG. 2016 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES); 2016.10.1109/IECBES.2016.7843459Search in Google Scholar
[47] Pedersen MEH, Chipperfield AJ. Simplifying particle swarm optimization. Appl Soft Comput 2010;10:618–28.10.1016/j.asoc.2009.08.029Search in Google Scholar
[48] Karaboga D, Basturk B. On the performance of artificial bee colony (ABC) algorithm. Appl Soft Comput 2008;8:687–97.10.1016/j.asoc.2007.05.007Search in Google Scholar
[49] Gandomia AH, Yang XS, Talatahari S, Alavi AH. Firefly algorithm with chaos. Commun Nonlinear Sci Numer Simulat 2013;18: 89–98.10.1016/j.cnsns.2012.06.009Search in Google Scholar
[50] McCall J. Genetic algorithms for modelling and optimisation. J Comput Appl Math 2005;184:205–22.10.1016/j.cam.2004.07.034Search in Google Scholar
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