Abstract
Background: The whale optimization algorithm is one of the popular meta-heuristic algorithms which has successfully been applied in various application areas such as image analysis and data clustering. However, the slow convergence rate and chances of sticking into the local optima due to improper balance of its exploration and exploitation phases are some of its pitfalls. Therefore, in this paper, a new improved whale optimization algorithm has been proposed. Moreover, the proposed method has been used in bag-of-features method for histopathological image classification.
Methods: The new algorithm, improved whale optimization algorithm, modifies the encircling phase of original whale optimization algorithm. The proposed algorithm has been used to cluster the extracted features for finding the relevant codewords to be used in the bag-of-features method for histopathological image classification.
Results: The efficiency of proposed algorithm has been analyzed on 23 benchmark functions in terms of mean fitness, standard deviation values, and convergence behavior. The performance of the improved whale optimization algorithm based histopathological image classification method has been analyzed on blue histology image dataset and compared with other meta-heuristic based bagof- features methods in terms of recall, precision, F-measure, and accuracy. The experimental results validate that the proposed method outperforms the considered state-of-the-art methods and attains 12% increase in the histopathological image classification accuracy.
Conclusion: In this paper, a new improved whale optimization algorithm has been proposed and applied in bag-of-features method for histopathological image classification. The results of proposed method outperform the other existing meta-heuristic methods over standard benchmark functions and histopathological image dataset.
Keywords: Whale optimization algorithm, histopathological image classification, bag-of-features, meta-heuristic methods, image dataset, data clustering.
Graphical Abstract
[1]
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].
[2]
J. Kennedy, “Particle swarm optimization”, Encycloped. Mach. learn., Springer: MA, USA, 2011, pp. 760-766. [DOI: https://doi.org/10.1007/978-0-387-30164-8_630]
[3]
J.C. Bansal, H. Sharma, S.S. Jadon, and M. Clerc, "Spider monkey optimization algorithm for numerical optimization", Memetic Comput., vol. 6, no. 1, pp. 31-47, 2014.
[4]
R.R. Chhikara, P. Sharma, and L. Singh, "A hybrid feature selection approach based on improved PSO and filter approaches for image steganalysis", Int. J. Mach. Learn. Cybern., vol. 7, pp. 1195-1206, 2016. [http://dx.doi.org/10.1007/s13042-015-0448-0].
[5]
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].
[6]
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].
[7]
E. Emary, H.M. Zawbaa, C. Grosan, and A.E. Hassenian, Feature subset selection approach by gray-wolf optimization., Afro-Euro. Conf. for Industrial Advancem, pp. 1-13. 2015 [http://dx.doi.org/10.1007/978-3-319-13572-4_1]
[8]
K. Hussain, M.N.M. Salleh, S. Cheng, and Y. Shi, "Metaheuristic research: a comprehensive survey", Artif. Intell. Rev., pp. 1-43, 2018.
[9]
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].
[10]
G. Kaur, and S. Arora, "Chaotic whale optimization algorithm", J. Computat. Des. Eng, vol. 5, pp. 275-284, 2018.
[11]
H.S. Alamri, Y.A. Alsariera, and K.Z. Zamli, "Opposition-based whale optimization algorithm", Adv. Sci. Lett., vol. 24, no. 10, pp. 7461-7464, 2018. [http://dx.doi.org/10.1166/asl.2018.12959].
[12]
H. Mittal, and M. Saraswat, "An optimum multi-level image thresholding segmentation using non-local means 2d histogram and exponential k best gravitational search algorithm", Eng. Appl. Artif. Intell., vol. 71, pp. 226-235, 2018. [http://dx.doi.org/10.1016/j.engappai.2018.03.001].
[13]
R. Pal, and M. Saraswat, Data clustering using enhanced biogeography-based optimization2017, Tenth International Conference on Contemporary Computing (IC3), Noida, India 2017, pp. 1-6. [http://dx.doi.org/10.1109/IC3.2017.8284305]
[14]
M.N. Gurcan, L.E. Boucheron, A. Can, A. Madabhushi, N.M. Rajpoot, and B. Yener, "Histopathological image analysis: a review", IEEE Rev. Biomed. Eng., vol. 2, pp. 147-171, 2009. [http://dx.doi.org/10.1109/RBME.2009.2034865]. [PMID: 20671804].
[15]
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. [http://dx.doi.org/10.1007/978-981-13-1595-4_18]
[16]
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].
[18]
N. Nayak, H. Chang, A. Borowsky, P. Spellman, and B. Parvin, Classification of tumor histopathology via sparse feature learning 2013, IEEE 10th International Symposium on Biomedical Imaging, San Francisco, CA, USA, pp. 410-413. 2013 [http://dx.doi.org/10.1109/ISBI.2013.6556499]
[19]
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].
[20]
M. Saraswat, and K.V. Arya, "Automated microscopic image analysis for leukocytes identification: a survey", Micron, vol. 65, pp. 20-33, 2014. [http://dx.doi.org/10.1016/j.micron.2014.04.001]. [PMID: 25041828].
[21]
N. Orlov, L. Shamir, T. Macura, J. Johnston, D.M. Eckley, and I.G. Goldberg, "WND-CHARM: multi-purpose image classification using compound image transforms", Pattern Recognit. Lett., vol. 29, no. 11, pp. 1684-1693, 2008. [http://dx.doi.org/10.1016/j.patrec.2008.04.013]. [PMID: 18958301].
[22]
H.L. Tang, R. Hanka, and H.H-S. Ip, "Histological image retrieval based on semantic content analysis", IEEE Trans. Inf. Technol. Biomed., vol. 7, no. 1, pp. 26-36, 2003. [http://dx.doi.org/10.1109/TITB.2003.808500]. [PMID: 12670016].
[23]
G. Diaz, and E. Romero, "Histopathological image classification using stain component features on a plsa model", In: Iberoamerican Congress on Pattern Recognition., Springer, pp. 55-62. 2010 [http://dx.doi.org/10.1007/978-3-642-16687-7_12]
[24]
U. Srinivas, H. Mousavi, C. Jeon, V. Monga, A. Hattel, and B. Jayarao, SHIRC: A simultaneous sparsity model for histopathological image representation and classification2013, IEEE 10th International Symposium on Biomedical Imaging, San Francisco, CA, USA 2013, pp. 1118-1121. [http://dx.doi.org/10.1109/ISBI.2013.6556675]
[25]
M. Ghosh, D. Das, C. Chakraborty, and A.K. Ray, "Automated leukocyte recognition using fuzzy divergence", Micron, vol. 41, no. 7, pp. 840-846, 2010. [http://dx.doi.org/10.1016/j.micron.2010.04.017]. [PMID: 20554209].
[26]
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].
[27]
A.A. Cruz-Roa, J.E.A. Ovalle, A. Madabhushi, and F.A.G. Osorio, "A deep learning architecture for image representation, visual interpretability and automated basal-cell carcinoma cancer detection", In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer , pp. 403-410. 2013 [http://dx.doi.org/10.1007/978-3-642-40763-5_50]
[28]
J. Xu, L. Xiang, Q. Liu, H. Gilmore, J. Wu, J. Tang, and A. Madabhushi, "Stacked sparse autoencoder (SSAE) for nuclei detection on breast cancer histopathology images", IEEE Trans. Med. Imaging, vol. 35, no. 1, pp. 119-130, 2016. [http://dx.doi.org/10.1109/TMI.2015.2458702]. [PMID: 26208307].
[29]
H. Chang, N. Nayak, P.T. Spellman, and B. Parvin, "Characterization of tissue histopathology via predictive sparse decomposition and spatial pyramid matching", In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp. 91-98. 2013. [http://dx.doi.org/10.1007/978-3-642-40763-5_12]
[30]
C. Malon, M. Miller, H.C. Burger, E. Cosatto, and H.P. Graf, "Identifying histological elements with convolutional neural networks", In: Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology, ACM: Cergy-Pontoise, France, pp. 450-456. 2008. [http://dx.doi.org/10.1145/1456223.1456316]
[31]
L. Hou, D. Samaras, T.M. Kurc, Y. Gao, J.E. Davis, and J.H. Saltz, "Patch-based convolutional neural network for whole slide tissue image classification", In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA 2016, pp. 2424-2433. [http://dx.doi.org/10.1109/CVPR.2016.266]
[32]
Y. Zhou, H. Chang, K. Barner, P. Spellman, and B. Parvin, "Classification of histology sections via multispectral convolutional sparse coding", In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA 2014, pp. 3081-3088. [http://dx.doi.org/10.1109/CVPR.2014.394]
[33]
J. Arevalo, A. Cruz-Roa, V. Arias, E. Romero, and F.A. González, "An unsupervised feature learning framework for basal cell carcinoma image analysis", Artif. Intell. Med., vol. 64, no. 2, pp. 131-145, 2015. [http://dx.doi.org/10.1016/j.artmed.2015.04.004]. [PMID: 25976208].
[34]
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].
[35]
J.C. Caicedo, A. Cruz, and F.A. Gonzalez, "Histopathology image classification using bag of features and kernel functions", In: Conference on Artificial Intelligence in Medicine in Europe, Springer, pp. 126-135. , 2009. [http://dx.doi.org/10.1007/978-3-642-02976-9_17]
[36]
M. Dholey, M. Maity, A. Sarkar, A. Giri, A. Sadhu, K. Chaudhury, S. Das, and J. Chatterjee, "Combining GMM-based hidden markov random field and bag-of-words trained classifier for lung cancer detection using pap-stained microscopic images", In: Advanced Computational and Communication Paradigms., Springer, pp. 695-705. 2018. [http://dx.doi.org/10.1007/978-981-10-8237-5_67]
[37]
S.H. Raza, R.M. Parry, Y. Sharma, Q. Chaudry, R.A. Moffitt, A. Young, and M.D. Wang, Automated classification of renal cell carcinoma subtypes using bag-of-features2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, Buenos Aires, Argentina, pp. 6749-6752. 2010. [http://dx.doi.org/10.1109/IEMBS.2010.5626009]
[38]
S.H. Raza, R.M. Parry, R.A. Moffitt, A.N. Young, and M.D. Wang, "An analysis of scale and rotation invariance in the bag-of-features method for histopathological image classification", In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer: Berlin Heidelber, pp. 66-74. 2011. [http://dx.doi.org/10.1007/978-3-642-23626-6_9]
[39]
R. Hernandez-Garcia, J. Ramos-Cozar, N. Guil, E. Garcia-Reyes, and H. Sahli, "Improving bag-of-visual-words model using visual n-grams for human action classification", Expert Syst. Appl., vol. 92, pp. 182-191, 2018. [http://dx.doi.org/10.1016/j.eswa.2017.09.016].
[40]
A. Cruz-Roa, G. Diaz, E. Romero, and F. A. Gonzalez, "Automatic annotation of histopathological images using a latent topic model based on non-negative matrix factorization", J. Pathol. Inform., vol. 2, . 2011. [http://dx.doi.org/10.4103/2153-3539.92031]
[41]
H. Bay, T. Tuytelaars, and L. van Gool, "Surf: Speeded up robust features", In: Leonardis A., Bischof H., and Pinz A., Eds., European Conf. Comp. Vis., Springer: Berlin, Heidelberg Vol. 3951, pp. 404-417. 2016
[42]
N. Dalal, and B. Triggs, "Histograms of oriented gradients for human detection", In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), San Diego, CA, USA, pp. 886-893. 2005. [http://dx.doi.org/10.1109/CVPR.2005.177]
[43]
A. Alahi, R. Ortiz, and P. Vandergheynst, "Freak: fast retina keypoint", In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA, pp. 510-517. 2012. [http://dx.doi.org/10.1109/CVPR.2012.6247715]
[44]
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].
[45]
A.K. Qin, V.L. Huang, and P.N. Suganthan, "Differential evolution algorithm with strategy adaptation for global numerical optimization", IEEE Trans. Evol. Comput., vol. 13, no. 2, pp. 398-417, 2009. [http://dx.doi.org/10.1109/TEVC.2008.927706].
[46]
S. Mirjalili, "The ant lion optimizer", Adv. Eng. Softw., vol. 83, pp. 80-98, 2015. [http://dx.doi.org/10.1016/j.advengsoft.2015.01.010].
[47]
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].
[48]
S. Saremi, S. Mirjalili, and A. Lewis, "Grasshopper optimisation algorithm: theory and application", Adv. Eng. Softw., vol. 105, pp. 30-47, 2017. [http://dx.doi.org/10.1016/j.advengsoft.2017.01.004].
[49]
G. Csurka, C. Dance, L. Fan, J. Willamowski, and C. Bray, "Visual categorization with bags of keypoints", In: Workshop on statistical learning in computer vision, ECCV , pp. 1-22. 2004
[50]
X. Yao, Y. Liu, and G. Lin, "Evolutionary programming made faster", IEEE Trans. Evol. Comput., vol. 3, no. 2, pp. 82-102, 1999. [http://dx.doi.org/10.1109/4235.771163].
[51]
"Blue histology, Available from:", http://www.lab.anhb.uwa.edu.au/ mb140/ [Accessed on 10 April 2017]
[52]
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].
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