Tumor Detection From Brain MRI Using Modified Sea Lion Optimization Based Kernel Extreme Learning Algorithm

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2020 by IJETT Journal
Volume-68 Issue-9
Year of Publication : 2020
Authors : Narendra Mohan
DOI :  10.14445/22315381/IJETT-V68I9P214

Citation 

MLA Style: Narendra Mohan "Tumor Detection From Brain MRI Using Modified Sea Lion Optimization Based Kernel Extreme Learning Algorithm" International Journal of Engineering Trends and Technology 68.9(2020):84-100. 

APA Style:Narendra Mohan. Tumor Detection From Brain MRI Using Modified Sea Lion Optimization Based Kernel Extreme Learning Algorithm  International Journal of Engineering Trends and Technology, 68(9),84-100.

Abstract
Major cause for high mortality in human being is brain tumor. Improper and delayed treatment leads to the development of malignant tumor which is untreatable. This realize us the necessity of tumor detection at earlier stage. For such early detection, initially the skull removal process is carried out in input MRI using Brain Surface Extraction (BSE) technique. The lesion enhancement process over the skull removed image is performed using Weiner filter. It is performed to attain better segmentation result. Next, the tumor region is segmented from the non-tumor part using region growing segmentation approach. Features are required to recognize whether the segmented tumor is benign or malignant. Therefore, SGLDM and LESH based feature extraction approaches are used in this method. The dimensionality of extracted features is reduced using feature selection process. Finally with that selected features the tumor classification is achieved using the MSLO based KELM approach. The effectiveness of proposed KELM-MSLO approach is determined using the benchmark datasets such as BRATS 2013 Leader board, BRATS 2014, 2015, and 2018. Finally, some performance metrics are evaluated to analyze the effective performance of presented technique on detection of tumor at former stage.

Reference

[1] E.S.A. El-Dahshan, H.M. Mohsen, K. Revett and, A.B.M,“Salem. Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm”, Expert systems with Applications., vol. 41, no.11,(2014), pp.5526- 5545.
[2] N.Nabizadeh, M.Kubat, N.John and C.Wright, “Efficacy of Gabor-Wavelet versus statistical features for brain tumor classification in MRI: A comparative study”, In Proceedings of the International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV) (p. 1). The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp),(2013).
[3] S.Vaishali, K.K.Rao and G.S. Rao, “A review on noise reduction methods for brain MRI images”, In 2015 International Conference on Signal Processing and Communication Engineering Systems,(2015) Jan 363-365
[4] Y.D.Zhang, S.Chen, S.H.Wang, J.F.Yang and P.Phillips, “Magnetic resonance brain image classification based on weighted?type fractional Fourier transform and nonparallel support vector machine”, International Journal of Imaging Systems and Technology., vol. 25, no. 4,(2015), pp.317- 327.
[5] Geethu, Mohan and M. M Subashini, “MRI based medical image analysis: Survey on brain tumor grade classification”, Biomedical Signal Processing and Control, vol. 39, (2018) , pp.139-161.
[6] S.Z.Oo and A.S.Khaing, “Brain tumor detection and segmentation using watershed segmentation and morphological operation”, International Journal of Research in Engineering and Technology., vol. 3, no.03,(2014),pp.367-374.
[7] N.Nabizadeh and M.Kubat,“Brain tumors detection and segmentation in MR images: Gabor wavelet vs. statistical features”, Computers & Electrical Engineering., vol. 45,(2015), pp.286-301.
[8] S.A Nagtode, B.B Potdukhe and P Morey, “Two dimensional discrete Wavelet transform and Probabilistic neural network used for brain tumor detection and classification”, In 2016 Fifth International Conference on Eco-friendly Computing and Communication Systems (ICECCS),IEEE(2016), pp. 20-26
[9] SHI Dongli, LI Qiang and G. Xin, “Brain tumor image segmentation algorithm based on convolution neural network and fuzzy inference system”, Journal of Frontiers of Computer Science and Technology, vol. 12, no. 4,(2018), pp. 608-617.
[10] B.CC, K.Rajamani and V.L.Lajish, “A review on automatic marker identification methods in watershed algorithms used for medical image segmentation”, IJISET-International J. Innov. Sci. Eng. Technol., ,vol. 2, (2015), no. 9.
[11] D.Kaur and, Y.Kaur, “Various image segmentation techniques: a review”, International Journal of Computer Science and Mobile Computing., vol. 3, no. 5,(2014), pp.809-814.
[12] B.LI and W.XIE, “An algorithm for image enhancement based on adaptive fractional differential using twodimensional Otsu standard”, Control Theory & Applications., vol. 32, no. 6, (2015),pp.794-800.
[13] M.I.Razzak, S.Naz and A.Zaib, “Deep learning for medical image processing: Overview, challenges and the future”, In Classification in BioApps, Springer, Cham, (2018), pp. 323-350.
[14] J.Amin, M.Sharif, N.Gul, M.Yasmin and S.A.Shad, “Brain tumor classification based on DWT fusion of MRI sequences using convolutional neural network”, Pattern Recognition Letters., vol. 129,(2020), pp.115-122.
[15] J.Amin, M.Sharif, M.Raza, T. Saba and M.A.Anjum, “Brain tumor detection using statistical and machine learning method”, Computer methods and programs in biomedicine., vol. 177,(2019),pp.69-79.
[16] F.Özyurt, E. Sertand D.Avc?, “An expert system for brain tumor detection: Fuzzy C-means with super resolution and convolutional neural network with extreme learning machine”, Medical hypotheses.,vol. 134,(2020), p.109433.
[17] M.Sharif, J.Amin, M.Raza, M.A.Anjum, H.Afzal and, S.A.Shad, “Brain tumor detection based on extreme learning”, Neural Computing and Applications, (2020), pp.1-13.
[18] S.Vijh, , S.Sharma and, P.Gaurav, “Brain Tumor Segmentation Using OTSU Embedded Adaptive Particle Swarm Optimization Method and Convolutional Neural Network”, In Data Visualization and Knowledge Engineering. Springer, Cham, (2020), pp. 171-194.
[19] M.Sharif, J.Amin, M.Raza, M.Yasmin and S.C.Satapathy, “An integrated design of particle swarm optimization (PSO) with fusion of features for detection of brain tumor”, Pattern Recognition Letters., vol. 129, (2020),pp.150-157.
[20] S.A.A.Ismael, A.Mohammed and H.Hefny, “An enhanced deep learning approach for brain cancer MRI images classification using residual networks”, Artificial Intelligence in Medicine., vol.102,(2020), p.101779.
[21] N.Abiwinanda, M.Hanif, S.T.Hesaputra, A.Handayani and T.R.Mengko, “Brain tumor classification using convolutional neural network”, In World Congress on Medical Physics and Biomedical Engineering .Springer, Singapore, (2018), pp. 183-189.
[22] A.Veeramuthu, S.Meenakshi andK.Ashok Kumar,“A neural network based deep learning approach for efficient segmentation of brain tumor medical image data”, Journal of Intelligent & Fuzzy Systems., vol. 36 no. 5,(2019), pp.4227-4234.
[23] K.U.Devi and R.Gomathi, “Brain tumour classification using saliency driven nonlinear diffusion and deep learning with convolutional neural networks (CNN)”, Journal of Ambient Intelligence and Humanized Computing,(2020), pp.1-11.
[24] N.S.M Raja, S. L. Fernandes, Nilanjan Dey, S.C Satapathy & V. Rajinikanth, “Contrast enhanced medical MRI evaluation using Tsallis entropy and region growing segmentation”, Journal of Ambient Intelligence and Humanized Computing, (2018), pp.1-12.
[25] R.Thillaikkarasi and, S.Saravanan, “An enhancement of deep learning algorithm for brain tumor segmentation using kernel based CNN with M-SVM”, Journal of medical systems., vol. 43, no. 4, (2019),p.84.
[26] S.K.Wajid and, A.Hussain, “Local energy-based shape histogram feature extraction technique for breast cancer diagnosis”, Expert Systems with Applications.,vol. 42, no. 20, (2015),pp.6990-6999.
[27] M.Mafarja, , I.Aljarah, , H.Faris, , A.I.Hammouri, , A.Z.Ala’M and, S.Mirjalili, “Binary grasshopper optimisation algorithm approaches for feature selection problems”, Expert Systems with Applications., vol. 117,(2019), pp.267-286.
[28] F.Mohanty, , S.Rup, , B.Dash, , B.Majhi and, M.N.S.Swamy, “An improved scheme for digital mammogram classification using weighted chaotic salp swarm algorithm-based kernel extreme learning machine”, Applied Soft Computing,(2020), p.106266.
[29] R.Masadeh, , B.A.Mahafzah and, A.Sharieh, “Sea Lion Optimization Algorithm”, Sea.,vol. 10, no.5, (2019).

Keywords
Brain Tumor, Segmentation, Kernel Extreme Learning Machine (KELM), Skull Removal, Region-Growing Technique.