Login Register Cart 0
Generic placeholder image

Recent Patents on Computer Science

Editor-in-Chief

ISSN (Print): 2213-2759
ISSN (Online): 1874-4796

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

Feature Selection for Histopathological Image Classification using levy Flight Salp Swarm Optimizer

Author(s): Venubabu Rachapudi* and Golagani Lavanya Devi

Volume 12, Issue 4, 2019

Page: [329 - 337] Pages: 9

DOI: 10.2174/2213275912666181210165129

Price: $65

Open Access Journals Promotions 2
conference banner
Abstract

Background: An efficient feature selection method for Histopathological image classification plays an important role to eliminate irrelevant and redundant features. Therefore, this paper proposes a new levy flight salp swarm optimizer based feature selection method.

Methods: The proposed levy flight salp swarm optimizer based feature selection method uses the levy flight steps for each follower salp to deviate them from local optima. The best solution returns the relevant and non-redundant features, which are fed to different classifiers for efficient and robust image classification.

Results: The efficiency of the proposed levy flight salp swarm optimizer has been verified on 20 benchmark functions. The anticipated scheme beats the other considered meta-heuristic approaches. Furthermore, the anticipated feature selection method has shown better reduction in SURF features than other considered methods and performed well for histopathological image classification.

Conclusion: This paper proposes an efficient levy flight salp Swarm Optimizer by modifying the step size of follower salp. The proposed modification reduces the chances of sticking into local optima. Furthermore, levy flight salp Swarm Optimizer has been utilized in the selection of optimum features from SURF features for the histopathological image classification. The simulation results validate that proposed method provides optimal values and high classification performance in comparison to other methods.

Keywords: Feature extraction, feature selection, meta-heuristic methods, salp swarm algorithm, histopathological image classification, levy flight.

« Previous Next »
Graphical Abstract

[1]
H. Mittal, and M. Saraswat, "Classification of histopathological images through bag-of-visual-words and gravitational search algorithm", In: International Conference Soft Computing for Problem Solving, Springer: Singapore, 2017, pp. 231-241.
[2]
H.S. Mousavi, V. Monga, G. Rao, and A.U. Rao, "Automated discrimination of lower and higher grade gliomas based on histopathological image analysis", J. Pathol. Inform., vol. 6, p. 15, 2015.
[3]
Y. Zheng, Z. Jiang, F. Xie, H. Zhang, Y. Ma, H. Shi, and Y. Zhao, "Feature extraction from histopathological images based on nucleus-guided convolutional neural network for breast lesion classification", Pattern Recognit., vol. 71, pp. 14-25, 2017. [http://dx.doi.org/10.1016/j.patcog.2017.05.010].
[4]
N. Dalal, and B. Triggs, "Histograms of oriented gradients for human detection", In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA 2005, pp. 886-893. [http://dx.doi.org/10.1109/CVPR.2005.177]
[5]
D.G. Lowe, "Distinctive image features from scale-invariant keypoints", Int. J. Comput. Vis., vol. 60, no. 2, pp. 91-110, 2004. [http://dx.doi.org/10.1023/B:VISI.0000029664.99615.94].
[6]
T. Ojala, M. Pietikäinen, and D. Harwood, "A comparative study of texture measures with classification based on featured distributions", Pattern Recognit., vol. 29, no. 1, pp. 51-59, 1996. [http://dx.doi.org/10.1016/0031-3203(95)00067-4].
[7]
T.H. Vu, H.S. Mousavi, V. Monga, G. Rao, and U.K. Rao, "Histopathological image classification using discriminative feature-oriented dictionary learning", IEEE Trans. Med. Imaging, vol. 35, no. 3, pp. 738-751, 2016. [http://dx.doi.org/10.1109/TMI.2015.2493530]. [PMID: 26513781].
[8]
M. Saraswat, and K.V. Arya, "Feature selection and classification of leukocytes using random forest", Med. Biol. Eng. Comput., vol. 52, no. 12, pp. 1041-1052, 2014. [http://dx.doi.org/10.1007/s11517-014-1200-8]. [PMID: 25284218].
[9]
J. Shi, J. Wu, Y. Li, Q. Zhang, and S. Ying, "Histopathological image classification with color pattern random binary hashing-based pcanet and matrix-form classifier", IEEE J. Biomed. Health Inform., vol. 21, no. 5, pp. 1327-1337, 2017. [http://dx.doi.org/10.1109/JBHI.2016.2602823]. [PMID: 27576270].
[10]
U. Srinivas, H.S. Mousavi, V. Monga, A. Hattel, and B. Jayarao, "Simultaneous sparsity model for histopathological image representation and classification", IEEE Trans. Med. Imaging, vol. 33, no. 5, pp. 1163-1179, 2014. [http://dx.doi.org/10.1109/TMI.2014.2306173]. [PMID: 24770920].
[11]
T.H. Vu, H.S. Mousavi, V. Monga, G. Rao, and U.K. Rao, "Histopathological image classification using discriminative feature-oriented dictionary learning", IEEE Trans. Med. Imaging, vol. 35, no. 3, pp. 738-751, 2016. [http://dx.doi.org/10.1109/TMI.2015.2493530]. [PMID: 26513781].
[12]
J. Xu, X. Luo, G. Wang, H. Gilmore, and A. Madabhushi, "A deep convolutional neural network for segmenting and classifying epithelial and stromal regions in histopathological images", Neurocomputing, vol. 191, pp. 214-223, 2016. [http://dx.doi.org/10.1016/j.neucom.2016.01.034]. [PMID: 28154470].
[13]
Y. Bengio, A. Courville, and P. Vincent, "Representation learning: A review and new perspectives", IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 8, pp. 1798-1828, 2013. [http://dx.doi.org/10.1109/TPAMI.2013.50]. [PMID: 23787338].
[14]
J. Masci, U. Meier, D. Cireşan, and J. Schmidhuber, "Stacked convolutional auto-encoders for hierarchical feature extraction", In: International Conference on Artificial Neural Networks, Springer 2011, pp. 52-59. [http://dx.doi.org/10.1007/978-3-642-21735-7_7]
[15]
N. Tajbakhsh, S.R. Gurudu, and J. Liang, "Automated polyp detection in colonoscopy videos using shape and context information", IEEE Trans. Med. Imaging, vol. 35, no. 2, pp. 630-644, 2016. [http://dx.doi.org/10.1109/TMI.2015.2487997]. [PMID: 26462083].
[16]
M. Dash, and H. Liu, "Feature selection for classification", Intell. Data Anal., vol. 1, no. 3, pp. 131-156, 1997. [http://dx.doi.org/10.3233/IDA-1997-1302].
[17]
I. Guyon, "J. Weston, S. Barnhill and V. Vapnik, “Gene selection for cancer classification using support vector machines", Mach. Learn., vol. 46, no. 1-3, pp. 389-422, 2002. [http://dx.doi.org/10.1023/A:1012487302797].
[18]
S. Bhattacharyya, A. Sengupta, T. Chakraborti, A. Konar, and D.N. Tibarewala, "Automatic feature selection of motor imagery EEG signals using differential evolution and learning automata", Med. Biol. Eng. Comput., vol. 52, no. 2, pp. 131-139, 2014. [http://dx.doi.org/10.1007/s11517-013-1123-9]. [PMID: 24165805].
[19]
H. Deng, and G. Runger, "Feature selection via regularized trees", In: The IEEE 2012 International Joint Conference on Neural Networks (IJCNN) IEEE, Brisbane, QLD, Australia, 2012, pp. 1-8.
[20]
M. Saraswat, and K.V. Arya, "Feature selection and classification of leukocytes using random forest", Med. Biol. Eng. Comput., vol. 52, no. 12, pp. 1041-1052, 2014. [http://dx.doi.org/10.1007/s11517-014-1200-8]. [PMID: 25284218].
[21]
M. Saraswat, K. Arya, and H. Sharma, "Leukocyte segmentation in tissue images using differential evolution algorithm", Swarm Evol. Comput., vol. 11, pp. 46-54, 2013. [http://dx.doi.org/10.1016/j.swevo.2013.02.003].
[22]
H. Mittal, and M. Saraswat, "An optimum multi-level image thresholding segmentation using non-local means 2D histogram and exponential kbest gravitational search algorithm", Eng. Appl. Artif. Intell., vol. 71, pp. 226-235, 2018. [http://dx.doi.org/10.1016/j.engappai.2018.03.001].
[23]
A.K. Tripathi, K. Sharma, and M. Bala, "A novel clustering method using enhanced grey wolf optimizer and mapreduce", Big Data Res., vol. 14, pp. 93-100, 2018.
[24]
H. Mittal, R. Pal, A. Kulhari, and M. Saraswat, "Chaotic kbest gravitational search algorithm (CKGSA)", In: IEEE 2016 Ninth International Conference on Con-temporary Computing (IC3), Noida, India 2016, pp. 1-6.
[25]
K. Jaiswal, H. Mittal, and S. Kukreja, "Randomized grey wolf optimizer (RGWO) with randomly weighted coefficients", In: IEEE 2017 Tenth International Conference on Contemporary Computing (IC3), Noida, India,2017, pp. 1-3.
[26]
M. Saraswat, and K. Arya, "Supervised leukocyte segmentation in tissue images using multi-objective optimization technique", Eng. Appl. Artif. Intell., vol. 31, pp. 44-52, 2014. [http://dx.doi.org/10.1016/j.engappai.2013.09.010].
[27]
A.C. Pandey, D.S. Rajpoot, and M. Saraswat, "Data clustering using hybrid improved cuckoo search method", In: IEEE 2016Ninth International Conference on Contemporary Computing (IC3), Noida, India, 2016, pp. 1-6. [http://dx.doi.org/10.1109/IC3.2016.7880195]
[28]
T. Ashish, S. Kapil, and B. Manju, Parallel bat algorithm-based clustering using MapReduce. Networking Communication and Data Knowledge Engineering., Springer, 2018, pp. 73-82. [http://dx.doi.org/10.1007/978-981-10-4600-1_7]
[29]
E. Rashedi, H. Nezamabadi-Pour, and S. Saryazdi, "GSA: A gravitational search algorithm", Inf. Sci., vol. 179, pp. 2232-2248, 2009. [http://dx.doi.org/10.1016/j.ins.2009.03.004].
[30]
E. Emary, H.M. Zawbaa, C. Grosan, and A.E. Hassenian, "Feature subset selection approach by gray-wolf optimization", In: Proceedings of Afro-European Conference for Industrial Advancement 2015, pp. 1-13.
[31]
F.G. Mohammadi, and M.S. Abadeh, "Image steganalysis using a bee colony based feature selection algorithm", Eng. Appl. Artif. Intell., vol. 31, pp. 35-43, 2014. [http://dx.doi.org/10.1016/j.engappai.2013.09.016].
[32]
J. C. Bansal, H. Sharma, S. S. Jadon, and M. Clerc, "Spider monkey optimization algorithm for numerical optimization", Memetic Computing, pp. 31-47. [http://dx.doi.org/10.1007/s12293-013-0128-0]
[33]
S. Kumar, B. Sharma, V.K. Sharma, and R.C. Poonia, Evol.Intel, . 2018
[34]
S. Kumar, B. Sharma, V.K. Sharma, H. Sharma, and J.C. Bansal, "Plant leaf disease identification using exponential spider monkey optimization", Sustainable Computing: Inform. Syst., 2018. [In press] [http://dx.doi.org/10.1016/j.suscom.2018.10.004]
[35]
S. Mirjalili, A.H. Gandomi, S.Z. Mirjalili, S. Saremi, H. Faris, and S.M. Mirjalili, "Salp swarm algorithm: A bio-inspired optimizer for engineering design problems", Adv. Eng. Softw., vol. 114, pp. 163-191, 2017. [http://dx.doi.org/10.1016/j.advengsoft.2017.07.002].
[36]
H. Faris, M.M. Mafarja, A.A. Heidari, and I. Aljarah, "A.Z. Ala M, S. Mirjalili, H. Fujita, An efficient binary salp swarm algorithm with crossover scheme for feature selection problems", Knowl. Base. Syst., vol. 154, pp. 43-67, 2018. [http://dx.doi.org/10.1016/j.knosys.2018.05.009].
[37]
G.I. Sayed, G. Khoriba, and M.H. Haggag, "A novel chaotic salp swarm algorithm for global optimization and feature selection", Appl. Intell., pp. 1-20, 2018. [http://dx.doi.org/10.1007/s10489-018-1158-6].
[38]
A.A. El-Fergany, "Extracting optimal parameters of PEM fuel cells using Salp Swarm Optimizer", Renew. Energy, vol. 119, pp. 641-648, 2018. [http://dx.doi.org/10.1016/j.renene.2017.12.051].
[39]
M. Mafarja, R. Jarrar, S. Ahmad, and A.A. Abusnaina, "Feature selection using binary particle swarm optimization with time varying inertia weight strategies", In: Proceedings of the 2nd International Conference on Future Networks and Distributed Systems, ACM 2018, p. 18. [http://dx.doi.org/10.1145/3231053.3231071]
[40]
S. Mirjalili, "Sca: A sine cosine algorithm for solving optimization problems", Knowl. Base. Syst., vol. 96, pp. 120-133, 2016. [http://dx.doi.org/10.1016/j.knosys.2015.12.022].
[41]
S. Mirjalili, "Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm", Knowl. Base. Syst., vol. 89, pp. 228-249, 2015. [http://dx.doi.org/10.1016/j.knosys.2015.07.006].
[42]
S. Mirjalili, and A. Lewis, "The whale optimization algorithm", Adv. Eng. Softw., vol. 95, pp. 51-67, 2016. [http://dx.doi.org/10.1016/j.advengsoft.2016.01.008].
[43]
R.N. Mantegna, "Fast, accurate algorithm for numerical simulation of Lévy stable stochastic processes", Phys. Rev. E Stat. Phys. Plasmas Fluids Relat. Interdiscip. Topics, vol. 49, no. 5, pp. 4677-4683, 1994. [http://dx.doi.org/10.1103/PhysRevE.49.4677]. [PMID: 9961762].
[44]
J. Kennedy, Particle swarm optimization. Encyclopedia of machine learning., Springer, 2011, pp. 760-766.
[45]
E. Rashedi, H. Nezamabadi-Pour, and S. Saryazdi, "Gsa: A gravitational search algorithm", Inf. Sci., vol. 179, no. 13, pp. 2232-2248, 2009. [http://dx.doi.org/10.1016/j.ins.2009.03.004].
[46]
X.S. Yang, A new metaheuristic bat-inspired algorithm. Nature inspired cooperative strategies for optimization (NICSO 2010)., Springer, 2010, pp. 65-74. [http://dx.doi.org/10.1007/978-3-642-12538-6_6]
[47]
X.S. Yang, "Flower pollination algorithm for global optimization", In: International Conference on Unconventional Computing and Natural Computation, Springer: Berlin, Heidelberg 2012, pp. 240-249. [http://dx.doi.org/10.1007/978-3-642-32894-7_27]
[48]
E. Cuevas, A. Echavarría, and M.A. Ramírez-Ortegón, "An optimization algorithm inspired by the states of matter that improves the balance between exploration and exploitation", Appl. Intell., vol. 40, no. 2, pp. 256-272, 2014. [http://dx.doi.org/10.1007/s10489-013-0458-0].
[49]
X.S. Yang, S.S.S. Hosseini, and A.H. Gandomi, "Firefly algorithm for solving non-convex economic dispatch problems with valve loading effect", Appl. Soft Comput., vol. 12, no. 3, pp. 1180-1186, 2012. [http://dx.doi.org/10.1016/j.asoc.2011.09.017].
[50]
Blue Histology, Available from:, http://www.lab.anhb.uwa.edu.au/mb140/Bluehistology
[51]
K. Sirinukunwattana, S.E. Ahmed Raza, Y-W. Tsang, D.R. Snead, I.A. Cree, and N.M. Rajpoot, "Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images", IEEE Trans. Med. Imaging, vol. 35, no. 5, pp. 1196-1206, 2016. [http://dx.doi.org/10.1109/TMI.2016.2525803]. [PMID: 26863654].

Rights & Permissions Print Cite
Article Metrics
39
3
© 2024 Bentham Science Publishers | Privacy Policy