Abstract
A novel modified adaptive sine cosine optimization algorithm (MASCA) integrated with particle swarm optimization (PSO) based local linear radial basis function neural network (LLRBFNN) model has been proposed for automatic brain tumor detection and classification. In the process of segmentation, the fuzzy C means algorithm based techniques drastically fails to remove noise from the magnetic resonance images. So, for reduction of noise and smoothening of brain tumor magnetic resonance image an improved fast and robust fuzzy c means algorithm segmentation algorithm has been proposed in this research work. The gray level co-occurrence matrix technique has been employed to extract features from brain tumor magnetic resonance images and the extracted features are fed as input to the proposed modified ASCA–PSO based LLRBFNN model for classification of benign and malignant tumors. In this research work the LLRBFNN model’s weights are optimized by using proposed MASCA–PSO algorithm which provides a unique solution to get rid of the hectic task of radiologist from manual detection. The classification accuracy results obtained from sine cosine optimization algorithm, PSO and adaptive sine cosine optimization algorithm integrated with particle swarm optimization based LLRBFNN models are compared with the proposed MASCA–PSO based LLRBFNN model. It is observed that the result obtained from the proposed model shows better classification accuracy results as compared to the other LLRBFNN based models.
Similar content being viewed by others
References
Ding Y, Fu X (2016) Kernel-based fuzzy c-means clustering algorithm based on genetic algorithm. Neurocomputing 188:233–238. https://doi.org/10.1016/j.neucom.2015.01.106
Pereira DC, Ramos RP, do Nascimento MZ (2014) Segmentation and detection of breast cancer in mammograms combining wavelet analysis and genetic algorithm. Comput Methods Programs Biomed 114(1):88–101. https://doi.org/10.1016/j.cmpb.2014.01.014
Mahapatra D (2017) Semi-supervised learning and graph cuts for consensus based medical image segmentation. Pattern Recognit 63:700709. https://doi.org/10.1016/j.patcog.2016.09.030
Bahadure NB, Ray AK, Thethi HP (2017) Image analysis for MRI based brain tumor detection and feature extraction using biologically inspired BWT and SVM. Hindawi Int J Biomed Imaging 2017, Article ID 9749108. https://doi.org/10.1155/2017/9749108
Satheeskumaran S, Sabrigiriraj M (2014) A new LMS based noise removal and DWT based R-peak detection in ECG signal for biotelemetry applications. Natl Acad Sci Lett 37(4):341–349. https://doi.org/10.1007/s40009-014-0238-3
Shanmuga Priya S, Valarmathi A (2018) Efficient fuzzy c-means based multilevel image segmentation for brain tumor detection in MR images. In: Design automation for embeded system. Springer, Berlin. https://doi.org/10.1007/s10617-017-9200-1. ISSN: 1572-8080
Javed A, Kim YC, Khoo MCK, Ward SLD, Nayak KS (2016) Dynamic 3-D MR visualization and detection of upper airway obstruction during sleep using region-growing segmentation. IEEE Trans Biomed Eng 63(2):431–437. https://doi.org/10.1109/TBME.2015.2462750
Abd-Ellah MK, Awad AI, Khalaf AM, Hamed FA (2016) Design and implementation of a computer-aided diagnosis system for brain tumor classification. In: 28th international conference on microelectronics (ICM), Cairo, pp 73–76
Li Z, Chen J (2015) Super pixel segmentation using linear spectral clustering. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), Boston, pp 1356–1363
Nandi AK, Basel AJ, Rui F (2015) Integrative cluster analysis in bioinformatics. Wiley, Berlin
Demirhan A, Güler I (2011) Combining stationary wavelet transform and self-organizing maps for brain MR image segmentation. Eng Appl Artif Intell 24:358–367. https://doi.org/10.1016/j.engappai.2010.09.008
Shree NV, Kumar TNR (2018) Identification and classification of brain tumor MRI images with feature extraction using DWT and probabilistic neural network. Brain Inform 5:23–30. https://doi.org/10.1007/s40708-017-0075-5
Chatzis SP, Varvarigou TA (2008) A fuzzy clustering approach toward hidden markov random field models for enhanced spatially constrained image segmentation. IEEE Trans Fuzzy Syst 16(5):1351–1361. https://doi.org/10.1109/TFUZZ.2008.2005008
Lei T, Jia X, Zhang Y, He L, Meng H, Nandi AK (2018) Significantly fast and robust fuzzy c-means clustering algorithm based on morphological reconstruction and membership filtering. IEEE Trans Fuzzy Syst 26(5):3027–3041. https://doi.org/10.1109/tfuzz.2018.2796074
Issa M, Hassanien AE, Oliva D, Helmi A, Ziedan I, Alzohairy A (2018) ASCA–PSO: adaptive sine cosine optimization algorithm integrated with particle swarm for pairwise local sequence alignment. Expert Syst Appl 99(1):56–70. https://doi.org/10.1016/j.eswa.2018.01.019
Ahmed MN, Yamany SM, Mohamed N, Farag AA, Moriarty T (2002) A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data. IEEE Trans Med Imaging 21(3):193–199. https://doi.org/10.1109/42.996338
Chen S, Zhang D (2004) Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure. IEEE Trans Syst Man Cybern B Cybern 34(4):1907–1916. https://doi.org/10.1109/tsmcb.2004.831165
Szilagyi L, Benyo Z, Szilagyii SM, Adam HS (2003) MR brain image segmentation using an enhanced fuzzy c-means algorithm. In: Proceeding of the 25th annual international conference of the IEEE EMBS, pp 17–21
Cai W, Chen S, Zhang D (2007) Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation. Pattern Recognit 40(3):825–838. https://doi.org/10.1016/j.patcog.2006.07.011
Krinidis S, Chatzis V (2010) A robust fuzzy local information c-means clustering algorithm. IEEE Trans Image Process 19(5):1328–1337. https://doi.org/10.1109/tip.2010.2040763
Gong M, Zhou Z, Ma J (2012) Change detection in synthetic aperture radar images based on image fusion and fuzzy clustering. IEEE Trans Image Process 21(4):2141–2151. https://doi.org/10.1109/TIP.2011.2170702
Gong M, Liang Y, Shi S, Ma J (2013) Fuzzy c-means clustering with local information and kernel metric for image segmentation. IEEE Trans Image Process 22(2):573–584. https://doi.org/10.1109/TIP.2012.2219547
Guo F, Wang X, Shen J (2016) Adaptive fuzzy c-means algorithm based on local noise detecting for image segmentation. IET Image Process 10(4):272–279. https://doi.org/10.1049/iet-ipr.2015.0236
Rezaei K, Agahi H (2017) Malignant and benign brain tumor segmentation and classification using SVM with weighted kernel width. Sig Image Proc Int J (SIPIJ). https://doi.org/10.5121/sipij.2017.8203
Torheim T, Malinen E, Kvaal K et al (2014) Classification of dynamic contrast enhanced MR images of cervical cancers using texture analysis and support vector machines. IEEE Trans Med Imaging 33(8):1648–1656. https://doi.org/10.1109/TMI.2014.2321024
Lang R, Zhao L, Jia K (2016) Brain tumor image segmentation based on convolution neural network. In: 2016 9th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI), Datong, pp 1402–1406
Deepa SN, Arunadevi B (2013) Extreme learning machine for classification of brain tumor in 3D MR images. Informatologia 46(2):111–121. ISSN 1330-0067
Krishna TG, Sunitha KVN, Mishra S (2018) Detection and classification of brain tumor from MRI medical image using wavelet transform and PSO based LLRBFNN algorithm. Int J Comput Sci Eng 6(1). https://doi.org/10.26438/ijcse/v6i1.1823. E-ISSN: 2347-2693
Nayak PK, Mishra S, Dash PK, Bisoi Ranjeeta (2016) Comparison of modified teaching–learning-based optimization and extreme learning machine for classification of multiple power signal disturbances. Neural Comput Appl 27(7):2107–2122. https://doi.org/10.1007/s00521-015-2010-0
Patra A, Das S, Mishra SN, Senapati MR (2017) An adaptive local linear optimized radial basis functional neural network model for financial time series prediction. Neural Comput Appl 28(1):101–110. https://doi.org/10.1007/s00521-015-2039-0
Liu B, Wang L, Jin YH (2007) An effective PSO-based memetic algorithm for flow shop scheduling. IEEE Trans Syst Man Cybern B Cybern 37(1):18–27. https://doi.org/10.1109/tsmcb.2006.883272
Senapati MR, Vijaya I, Dash PK (2007) Rule extraction by training radial basis functional neural network with particle swarm optimization. Am J Sci 3(8):592–599. ISSN: 1549-3636
Yang X-S, Deb S, Fong S (2011) Accelerated particle swarm optimization and support vector machine for business optimization and applications. In: International conference on networked digital technologies, NDT 2011. Communications in computer and information science, vol 136, pp 53–66. Springer, Berlin
Kaur T, Saini BS, Gupta S (2016) Optimized multi threshold brain tumor image segmentation using two dimensional minimum cross entropy based on co-occurrence matrix. In: Medical imaging in clinical applications. Part of the studies in computational intelligence, vol 651. Springer, Berlin, pp 461–486. https://doi.org/10.1007/978-3-319-33793-7_20
Garg H (2016) A hybrid PSO–GA algorithm for constrained optimization problems. Appl Math Comput 274(1):292–305. https://doi.org/10.1016/j.amc.2015.11.001
de Fátima Araújoa T, Uturbey W (2013) Performance assessment of PSO, DE and hybrid PSO–DE algorithms when applied to the dispatch of generation and demand. Int J Electr Power Energy Syst 47:205–217. https://doi.org/10.1016/j.ijepes.2012.11.002
Santra D, Mukherjee A, Sarker K, Chatterjee D (Oct 2016) Hybrid PSO–ACO algorithm to solve economic load dispatch problem with transmission loss for small scale power system. In: 2016 international conference on intelligent control power and instrumentation (ICICPI), pp 21–23
Mirjalili S (2016) SCA: a sine cosine algorithm for solving optimization problems. Knowl Based Syst 96:120–133. https://doi.org/10.1016/j.knosys.2015.12.022
Tasnin W, Saikia LC (2018) Maiden application of an sine–cosine algorithm optimised FO cascade controller in automatic generation control of multi-area thermal system incorporating dish-Stirling solar and geothermal power plants. IET Renew Power Gener 12(5):585–597. https://doi.org/10.1049/iet-rpg.2017.0063
Nenavath H, Jatoth RK, Das S (2018) A synergy of the sine–cosine algorithm and particle swarm optimizer for improved global optimization and object tracking. Swarm Evol Comput. https://doi.org/10.1016/j.swevo.2018.02.011
Gonçalves H, Gonçalves JA, Corte-Real L (2011) HAIRIS: a method for automatic image registration through histogram-based image segmentation. IEEE Trans Image Process 20(3):776–789. https://doi.org/10.1109/TIP.2010.2076298
Jamil M, Yang X-S (2013) A literature survey of benchmark functions for global optimization problems. Int J Math Model Numer Optim 4(2):150–194. https://doi.org/10.1504/IJMMNO.2013.055204
Smith TM, Bonacuse P, Sosa J, Kulis M, Evans L (2018) A quantifiable and automated volume fraction characterization technique for secondary and tertiary γ′ precipitates in Ni-based super alloys. Mater Charact 140:86–94. https://doi.org/10.1016/j.matchar.2018.03.051
Pal C, Das P, Chakrabarti A, Ghosh R (2017) Rician noise removal in magnitude MRI images using efficient anisotropic diffusion filtering. Int J Imaging Syst Technol 27(3):248–264. https://doi.org/10.1002/ima.22230
Aja-Fernandez S, Alberola-Lopez C, Westin C-F (2008) Noise and signal estimation in magnitude MRI and Rician distributed images: A LMMSE approach. IEEE Trans Image Process 17(8):1383–1398. https://doi.org/10.1109/tip.2008.925382
Dataset: Webpage of Medical School of Harvard University. www.med.harvard.edu/AANLIB/home.html
Cocosco CA, Kollokian V, Kwan RK-S, Evans AC (2011). BrainWeb: online interface to a 3D MRI simulated brain database (Online). http://www.bic.mni.mcgill.ca/brainweb
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. https://doi.org/10.1109/TIP.2003.819861
Aja-Fernandez S, San-José-Estépar R, Alberola-Lopez C, Westin C (Sept 2006) Image quality assessment based on local variance. In: Proceeding of the 28th IEEE EMBS, New York, pp 4815–4818
Nenavatha H, Jatotha RK, Das S (2018) A synergy of the sine–cosine algorithm and particle swarm optimizer for improved global optimization and object tracking. Swarm Evol Comput 43:1–30. https://doi.org/10.1016/j.swevo.2018.02.011
Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM (2017) Slap swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191. https://doi.org/10.1016/j.advengsoft.2017.07.002
Mirjalili S, Mirjalili SM, Lewis A (2014) Gary wolf optimizer. Adv Eng Softw 69:46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007
Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008
Mirjalili S (2015) Mouth –flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl Based Syst 89:228–249. https://doi.org/10.1016/j.knosys.2015.07.006
Xu Y, Fan P, Yuan L (2013) A simple and efficient artificial bee colony algorithm. Math Probl Eng 2013:1–9, Article ID 526315, Hindawi. http://dx.doi.org/10.1155/2013/526315
Mahesh KM, Renjit JA (2018) Evolutionary intelligence for brain tumor recognition from MRI images: a critical study and review. Evol Intell. https://doi.org/10.1007/s12065-018-0156-2
Nayak DR, Dash R, Majhi B (2016) Brain MR image classification using two-dimensional discrete wavelet transform and AdaBoost with random forests. Neurocomputing 117:188–197. https://doi.org/10.1016/j.neucom.2015.11.034i
Mohana G, Subashini MM (2018) MRI based medical image analysis: survey on brain tumor grade classification. Biomed Signal Process Control 39:139–161
Mohanaiah P, Sathyanarayana P, GuruKumar L (2013) Image texture feature extraction using GLCM approach. Int J Sci Res Publ 3(5):1
Das S, Chowdhury M, Kundu MK (2013) Brain MR image classification using multiscale geometric analysis of ripplet. Prog Electromagn Res 137:1–17. https://doi.org/10.2528/PIER13010105
Nayak DR, Dash R, Majhi B (2017) Discrete ripplet-II transform and modified PSO based improved evolutionary extreme learning machine for pathological brain detection. Neurocomputing. https://doi.org/10.1016/j.neucom.2017.12.030
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Mishra, S., Sahu, P. & Senapati, M.R. MASCA–PSO based LLRBFNN model and improved fast and robust FCM algorithm for detection and classification of brain tumor from MR image. Evol. Intel. 12, 647–663 (2019). https://doi.org/10.1007/s12065-019-00266-x
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12065-019-00266-x