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Cervical Cytology Classification Using PCA and GWO Enhanced Deep Features Selection

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Abstract

Cervical cancer is one of the most deadly and common diseases among women worldwide. It is completely curable if diagnosed in an early stage, but the tedious and costly detection procedure makes it unviable to conduct population-wise screening. Thus, to augment the effort of the clinicians, in this paper, we propose a fully automated framework that utilizes deep learning and feature selection using evolutionary optimization for cytology image classification. The proposed framework extracts deep feature from several convolution neural network (CNN) models and uses a two-step feature reduction approach to ensure reduction in computation cost and faster convergence. The features extracted from the CNN models form a large feature space whose dimensionality is reduced using principal component analysis while preserving 99% of the variance. A non-redundant, optimal feature subset is selected from this feature space using an evolutionary optimization algorithm, the grey wolf optimizer, thus improving the classification performance. Finally, the selected feature subset is used to train an support vector machine classifier for generating the final predictions. The proposed framework is evaluated on three publicly available benchmark datasets: Mendeley Liquid Based Cytology (4-class) dataset, Herlev Pap Smear (7-class) dataset, and the SIPaKMeD Pap Smear (5-class) dataset achieving classification accuracies of 99.47, 98.32 and 97.87%, respectively, thus justifying the reliability of the approach. The relevant codes for the proposed approach can be found in: https://github.com/DVLP-CMATERJU/Two-Step-Feature-Enhancement.

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References

  1. Akter L, Islam MM, Al-Rakhami MS, Haque MR, et al. Prediction of cervical cancer from behavior risk using machine learning techniques. SN Comput Sci. 2021;2(3):1–10.

    Article  Google Scholar 

  2. AlMubarak HA, Stanley J, Guo P, Long R, Antani S, Thoma G, Zuna R, Frazier S, Stoecker W. A hybrid deep learning and handcrafted feature approach for cervical cancer digital histology image classification. Int J Healthc Inf Syst Inform. 2019;14(2):66–87.

    Article  Google Scholar 

  3. Azaza A, Abdellaoui M, Douik A. Off-the-shelf deep features for saliency detection. SN Comput Sci. 2021;2(2):1–10.

    Article  Google Scholar 

  4. Basak H, Kundu R. Comparative study of maturation profiles of neural cells in different species with the help of computer vision and deep learning. In: International symposium on signal processing and intelligent recognition systems. Springer; 2020. p. 352–66.

  5. Basak H, Kundu R, Agarwal A, Giri S. Single image super-resolution using residual channel attention network. In: 2020 IEEE 15th international conference on industrial and information systems (ICIIS). IEEE; (2020). p. 219–24.

  6. Bora K, Chowdhury M, Mahanta LB, Kundu MK, Das AK. Automated classification of pap smear images to detect cervical dysplasia. Comput Methods Programs Biomed. 2017;138:31–47.

    Article  Google Scholar 

  7. Byriel J. Neuro-fuzzy classification of cells in cervical smears. Master’s Thesis, Technical University of Denmark: Oersted-DTU, Automation. 1999.

  8. Chankong T, Theera-Umpon N, Auephanwiriyakul S. Automatic cervical cell segmentation and classification in Pap smears. Comput Methods Programs Biomed. 2014;113(2):539–56.

    Article  Google Scholar 

  9. Chattopadhyay S, Basak H. Multi-scale attention U-Net (MsAUNeT): a modified U-Net architecture for scene segmentation. 2020. arXiv:200906911.

  10. De Jong KA. Analysis of the behavior of a class of genetic adaptive systems. Technical report. 1975.

  11. Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. ImageNet: a large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. IEEE; 2009. p. 248–55.

  12. Dey S, Das S, Ghosh S, Mitra S, Chakrabarty S, Das N. SynCGAN: using learnable class specific priors to generate synthetic data for improving classifier performance on cytological images. In: Communications in computer and information science. Springer Singapore; 2020. p. 32–42. https://doi.org/10.1007/978-981-15-8697-2_3.

  13. Erlich I, Venayagamoorthy GK, Worawat N. A mean-variance optimization algorithm. In: IEEE congress on evolutionary computation. IEEE; 2010. p. 1–6.

  14. Ferlay J, Colombet M, Soerjomataram I, Mathers C, Parkin D, Piñeros M, Znaor A, Bray F. Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods. Int J Cancer. 2019;144(8):1941–53.

    Article  Google Scholar 

  15. GençTav A, Aksoy S, ÖNder S. Unsupervised segmentation and classification of cervical cell images. Pattern Recognit. 2012;45(12):4151–68.

    Article  Google Scholar 

  16. He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2016. p. 770–8.

  17. Holland JH, et al. Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT Press; 1992.

    Book  Google Scholar 

  18. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ. Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2017. p. 4700–8.

  19. Huang J, Wang T, Zheng D, He Y. Nucleus segmentation of cervical cytology images based on multi-scale fuzzy clustering algorithm. Bioengineered. 2020;11(1):484–501.

    Article  Google Scholar 

  20. Hussain E, Mahanta LB, Borah H, Das CR. Liquid based-cytology Pap smear dataset for automated multi-class diagnosis of pre-cancerous and cervical cancer lesions. Data Brief. 2020;30:105589.

    Article  Google Scholar 

  21. Jantzen J, Norup J, Dounias G, Bjerregaard B. Pap-smear benchmark data for pattern classification. In: Nature inspired smart information systems (NiSIS 2005); 2005. p 1–9.

  22. Kennedy J, Eberhart R. Particle swarm optimization. In: Proceedings of ICNN’95-international conference on neural networks, vol. 4. IEEE; 1995. p. 1942–8.

  23. Marinakis Y, Dounias G, Jantzen J. Pap smear diagnosis using a hybrid intelligent scheme focusing on genetic algorithm based feature selection and nearest neighbor classification. Comput Biol Med. 2009;39(1):69–78.

    Article  Google Scholar 

  24. Martínez-Más J, Bueno-Crespo A, Martínez-España R, Remezal-Solano M, Ortiz-González A, Ortiz-Reina S, Martínez-Cendán JP. Classifying Papanicolaou cervical smears through a cell merger approach by deep learning technique. Expert Syst Appl. 2020;160:113707.

    Article  Google Scholar 

  25. Mirjalili S. Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl Based Syst. 2015;89:228–49.

    Article  Google Scholar 

  26. Mirjalili S, Lewis A. The whale optimization algorithm. Adv Eng Softw. 2016;95:51–67.

    Article  Google Scholar 

  27. Mirjalili S, Mirjalili SM, Lewis A. Grey wolf optimizer. Adv Eng Softw. 2014;69:46–61.

    Article  Google Scholar 

  28. Mitra S, Dey S, Das N, Chakrabarty S, Nasipuri M, Naskar MK. Identification of malignancy from cytological images based on superpixel and convolutional neural networks. In: Studies in computational intelligence. Springer Singapore; 2019. p. 103–22. https://doi.org/10.1007/978-981-13-7334-3_8.

  29. Mitra S, Das N, Dey S, Chakrabarty S, Nasipuri M, Naskar MK. Cytology image analysis techniques towards automation: systematically revisited. 2020. arXiv:2003.07529.

  30. Niedzielewski K, Marchwiany ME, Piliszek R, Michalewicz M, Rudnicki W. Multidimensional feature selection and high performance parallex. SN Comput Sci. 2020;1(1):1–7.

    Article  Google Scholar 

  31. Plissiti ME, Dimitrakopoulos P, Sfikas G, Nikou C, Krikoni O, Charchanti A. SIPAKMED: a new dataset for feature and image based classification of normal and pathological cervical cells in Pap smear images. In: 2018 25th IEEE international conference on image processing (ICIP). IEEE; 2018. p. 3144–8.

  32. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. 2014. arXiv:1409.1556.

  33. Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna ZB. Rethinking the Inception Architecture for Computer Vision. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2016). https://doi.org/10.1109/cvpr.2016.308.

  34. Wang XY, Garibaldi JM. Simulated annealing fuzzy clustering in cancer diagnosis. Informatica. 2005;29:61–70.

    Google Scholar 

  35. William W, Ware A, Basaza-Ejiri AH, Obungoloch J. Cervical cancer classification from Pap-smears using an enhanced fuzzy C-means algorithm. Inform Med Unlocked. 2019;14:23–33.

    Article  Google Scholar 

  36. William W, Ware A, Basaza-Ejiri AH, Obungoloch J. A pap-smear analysis tool (PAT) for detection of cervical cancer from pap-smear images. Biomed Eng Online. 2019;18(1):16.

    Article  Google Scholar 

  37. Win KP, Kitjaidure Y, Hamamoto K, Myo Aung T. Computer-assisted screening for cervical cancer using digital image processing of Pap smear images. Appl Sci. 2020;10(5):1800.

    Article  Google Scholar 

  38. Wu M, Yan C, Liu H, Liu Q, Yin Y. Automatic classification of cervical cancer from cytological images by using convolutional neural network. Biosci Rep. 2018;38(6). https://doi.org/10.1042/BSR20181769.

  39. Xue D, Zhou X, Li C, Yao Y, Rahaman MM, Zhang J, Chen H, Zhang J, Qi S, Sun H. An application of transfer learning and ensemble learning techniques for cervical histopathology image classification. IEEE Access. 2020;8:104603–18.

    Article  Google Scholar 

  40. Yang XS. Firefly algorithms for multimodal optimization. In: International symposium on stochastic algorithms. Springer; 2009. p. 169–78.

  41. Yang XS, Gandomi AH. Bat algorithm: a novel approach for global engineering optimization. Eng Comput. 2012.

  42. Zhang L, Kong H, Ting Chin C, Liu S, Fan X, Wang T, Chen S. Automation-assisted cervical cancer screening in manual liquid-based cytology with hematoxylin and eosin staining. Cytom Part A. 2014;85(3):214–30.

    Article  Google Scholar 

  43. Zhang L, Lu L, Nogues I, Summers RM, Liu S, Yao J. DeepPap: deep convolutional networks for cervical cell classification. IEEE J Biomed Health Inform. 2017;21(6):1633–43.

    Article  Google Scholar 

  44. Zhang Y. Support vector machine classification algorithm and its application. In: International conference on information computing and applications. Springer; 2012. p. 179–86.

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Acknowledgements

The work is supported by SERB (DST), Govt. of India (Ref. no. EEQ/2018/000963).

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Authors and Affiliations

  1. Department of Electrical Engineering, Jadavpur University, 188, Raja S.C. Mullick Road, Jadavpur, Kolkata, West Bengal, 700032, India

    Hritam Basak & Rohit Kundu

  2. Theism Medical Diagnostics Centre, Dum Dum, Kolkata, West Bengal, 700030, India

    Sukanta Chakraborty

  3. Department of Computer Science and Engineering, Jadavpur University, 188, Raja S.C. Mullick Road, Jadavpur, Kolkata, West Bengal, 700032, India

    Nibaran Das

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  1. Hritam Basak
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Correspondence to Nibaran Das.

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This article is part of the topical collection “AI and Deep Learning Trends in Healthcare” guest edited by KC Santosh, Paolo Soda and Zalelam Temesgen.

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Basak, H., Kundu, R., Chakraborty, S. et al. Cervical Cytology Classification Using PCA and GWO Enhanced Deep Features Selection. SN COMPUT. SCI. 2, 369 (2021). https://doi.org/10.1007/s42979-021-00741-2

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