Skip to main content
SpringerLink
Log in

Cervical cell classification based on the CART feature selection algorithm

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

In recent years, conventional artificial method leads to low efficiency in the classification of cervical cell, which requires professional completion. Therefore, the classification process is increasingly dependent on artificial intelligence. The traditional image classification method needs to extract a large number of features. Redundant features cause the recognition speed to be slow, and influence the recognition effect. To address these problems and obtain the highest recognition accuracy with the least number of features, this paper proposes a machine learning method based on feature selection algorithm for cervical cell classification. Firstly, we introduced classification and regression trees (CART) for cell feature selection, which reduces the dimension of input feature attributes. Subsequently, particle swarm optimization (PSO) was used to optimize the hyperparameters of support vector machine (SVM) in this paper, making the SVM model better for classification. Finally, the Herlev dataset was introduced to verify the classification performance. The experimental results show that the proposed algorithm can extract accurate and effective features and obtain high classification accuracy, thus verifying the effectiveness of the proposed algorithm. Moreover, the network structure of the proposed algorithm is relatively simple with a low computation cost, which makes it feasible of further extension to the classification application of other cancer cells.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Abeel T, Helleputte T, Peer Y et al (2010) Robust biomarker identification for cancer diagnosis with ensemble feature selection methods. Bioinformatics 26:392–398

    Article  Google Scholar 

  • Boloncanedo V, Remeseiro B (2020) Feature selection in image analysis: a survey. Artif Intell Rev 53:2905–2931

    Article  Google Scholar 

  • Bora K, Chowdhury M, Mahanta L et al (2017) Automated classification of pap smear images to detect cervical dysplasia. Comput Methods Programs Biomed 138:31–47

    Article  Google Scholar 

  • Chankong T, Theera-Umpon N, Auephanwiriyakul S (2009) Cervical cell classification using Fourier transform. In: 13th international conference on biomedical engineering, vol 23, pp 476–480

  • Chankong T, Theera-Umpon N, Auephanwiriyankul S (2014) Automatic cervical cell segmentation and classification in pap smears. Comput Methods Programs Biomed 113:539–556

    Article  Google Scholar 

  • Chen QX, Liu Q (2010) Textural feature analysis for ultrasound breast tumor images. In: 2010 4th international conference on bioinformatics and biomedical engineering, pp 1–4. https://doi.org/10.1109/ICBBE.2010.5516918

  • Chen YF, Huang PC, Lin KC et al (2014) Semi-automatic segmentation and classification of pap smear cells. IEEE J Biomed Health Inform 18:94–108

    Article  Google Scholar 

  • Ferlay J, Soerjomataram I, Dikshit R et al (2015) Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer 136:359–386. https://doi.org/10.1002/ijc.29210

    Article  Google Scholar 

  • Gasparovicaasite M, Polaka I, Alekseyeva L (2016) The impact of feature selection on the information held in bioinformatics data. Inf Technol Manag Sci 18:115–121. https://doi.org/10.1515/itms-2015-0018

  • Geurts P, Fillet M, Seny D et al (2005) Proteomic mass spectra classification using decision tree based ensemble methods. Bioinformatics 21:3138–3145

    Article  Google Scholar 

  • Haralick RM, Shanmugam K, Dinstein I (1973) Textural features for image classification. Syst Man Cybern 3:610–621

    Article  Google Scholar 

  • Harimoorthy K, Thangavelu M (2020) Multi-disease prediction model using improved SVM-radial bias technique in healthcare monitoring system. J Ambient Intell Hum Comput. https://doi.org/10.1007/s12652-019-01652-0

    Article  Google Scholar 

  • Hemavathi D, Srimathi H (2020) Effective feature selection technique in an integrated environment using enhanced principal component analysis. J Ambient Intell Hum Comput. https://doi.org/10.1007/s12652-019-01647-x

    Article  Google Scholar 

  • Hitam NA, Ismail AR, Saeed F (2019) An optimized support vector machine (SVM) based on particle swarm optimization (PSO) for cryptocurrency forecasting. Proc Comput Sci 163:427–433

    Article  Google Scholar 

  • Hong JG, Cheng DX, Yu GP (1983) Characteristics of cell image. Inf Control 12:28–33

    Google Scholar 

  • Hu LP (2018) Principal component analysis application (I)-principal component regression analysis. Sichuan Mental Health 2:128–132

    Google Scholar 

  • Huang CL, Dun JF (2008) A distributed PSO-SVM hybrid system with feature selection and parameter optimization. Appl Soft Comput 8:1381–1391

    Article  Google Scholar 

  • Huang H, Wang ZJ, Chung WY (2019) Efficient parameter selection for support vector machines. Enterprise Inf Syst 13:916–932

    Article  Google Scholar 

  • Jantzen J, Norup J, Dounias G, Bjerregaard B (2005) Pap-smear benchmark data for pattern classification. In: Proceedings of the European symposium on nature inspired smart information systems, pp 1–9

  • Kim TW, Koh DH, Park CY (2010) Decision tree of occupational lung cancer using classification and regression analysis. Saf Health Work 1:140–148

    Article  Google Scholar 

  • Kong AL, Pezzin LE, Nattinger AB (2015) Identifying patterns of breast cancer care provided at high-volume hospitals: a classification and regression tree analysis. Breast Cancer Res Treat 153:689–698

    Article  Google Scholar 

  • Krzywinski M, Altman N (2017) Classification and regression trees. Nat Methods 14:757–758

    Article  Google Scholar 

  • Lee IH, Lushington GH, Visvanathan M (2011) A filter-based feature selection approach for identifying potential biomarkers for lung cancer. J Clin Bioinform 1:11–11

    Article  Google Scholar 

  • Lohrmann C, Luukka P, Jablonskasabuka M, Kauranne T (2018) A combination of fuzzy similarity measures and fuzzy entropy measures for supervised feature selection. Expert Syst Appl 110:216–236

    Article  Google Scholar 

  • Lorena LHN, Carvalho ACPLF, Lorena AC (2015) Filter feature selection for one-class classification. J Intell Rob Syst 80:227–243

    Article  Google Scholar 

  • Ma SS, Dong ML, Zhang F (2017) Research on flow cytometry data grouping method based on kernel principal component analysis. J Biomed Eng 1:115–122

    Google Scholar 

  • Mariarputham EJ, Stephen A (2015) Nominated texture based cervical cancer classification. Comput Math Methods Med 2015:586928–586928

    Article  Google Scholar 

  • Mavroforakis M, Georgiou HV, Dimitropoulos N et al (2006) Mammographic masses characterization based on localized texture and dataset fractal analysis using linear, neural and support vector machine classifiers. Artif Intell Med 37:145–162

    Article  Google Scholar 

  • Miao JY, Niu LF (2016) A survey on feature selection. Proc Comput Sci 91:919–926

    Article  Google Scholar 

  • Moslehi F, Haeri A (2020) A novel hybrid wrapper-filter approach based on genetic algorithm, particle swarm optimization for feature subset selection. J Ambient Intell Hum Comput 11:1105–1127

    Article  Google Scholar 

  • Patel J, Gamit N (2016) A review on feature extraction techniques in content based image retrieval. In: 2016 international conference on wireless communications, signal processing and networking. https://doi.org/10.1109/WiSPNET.2016.7566544

  • Plissiti ME, Nikou C (2013) A review of automated techniques for cervical cell image analysis and classification. In: Andreaus U, Iacoviello D (eds) Biomedical imaging and computational modeling in biomechanics. Springer, Netherlands, pp 1–18

    Google Scholar 

  • Raghavan V, Rao RK (2015) A semi-automated morphometric assessment of nuclei in pap smears using ImageJ. J Evol Med Dent Sci 4:5363–5370

    Article  Google Scholar 

  • Rahman TY, Mahanta LB, Das AK, Sarma JD (2020) Automated oral squamous cell carcinoma identification using shape, texture and color features of whole image strips. Tissue Cell 63:101322–101322

    Article  Google Scholar 

  • Sajeena TA, Jereesh AS (2015) Automated cervical cancer detection through RGVF segmentation and SVM classification. In: 2015 international conference on computing and network communications. https://doi.org/10.1109/CoCoNet.2015.7411260

  • Seijopardo B, Portodiaz I, Boloncanedo V, Alonsobetanzos A (2017) Ensemble feature selection: homogeneous and heterogeneous approaches. Knowl-Based Syst 118:124–139

    Article  Google Scholar 

  • Stewart BW, Wild CP (2014) World cancer report 2014. World Health Organization. https://publications.iarc.fr/Non-Series-Publications/World-Cancer-Reports/World-Cancer-Report-2014

  • Tsantis S, Cavouras D, Kalatzis I et al (2005) Development of a support vector machine-based image analysis system for assessing the thyroid nodule malignancy risk on ultrasound. Ultrasound Med Biol 31:1451–1459

    Article  Google Scholar 

  • Wang P, Wang LR, Li YM et al (2019a) Automatic cell nuclei segmentation and classification of cervical pap smear images. Biomed Signal Process Control 48:93–103

    Article  Google Scholar 

  • Wang XK, Guan SY, Hua L et al (2019b) Classification of spot-welded joint strength using ultrasonic signal time frequency features and PSO-SVM method. Ultrasonics 91:161–169

    Article  Google Scholar 

  • Wu W, Zhou H (2017) Data-driven diagnosis of cervical cancer with support vector machine-based approaches. IEEE Access 5:25189–25195

    Article  Google Scholar 

  • Yao MH, Wang N, Qi M, Li Y (2014) Research on improved maximum correlation minimum redundancy feature selection method. Comput Eng Appl 24:116–122

    Google Scholar 

  • Zhang CJ, Yang YJ, Du ZW, Ma C (2016) Particle swarm optimization algorithm based on ontology model to support cloud computing applications. J Ambient Intell Hum Comput 7:633–638

    Article  Google Scholar 

  • Zhang L, Lu L, Nogues I et al (2017) Deep pap: deep convolutional networks for cervical cell classification. IEEE J Biomed Health Inform 21:1633–1643

    Article  Google Scholar 

  • Zhang ZJ, Song FX, Zhang P et al (2018) A new online field feature selection algorithm based on streaming data. J Ambient Intell Hum Comput. https://doi.org/10.1007/s12652-018-0959-0

    Article  Google Scholar 

  • Zhao LL, Sun LY, Yin JP (2017) Cervical cell recognition combined with hierarchical method and principal component analysis. J Natl Univ Defense Technol 6:45–50

    Google Scholar 

  • Zhao M, Wu AG, Song JJ et al (2016) Automatic screening of cervical cells using block image processing. Biomed Eng Online 15:14–14

    Article  Google Scholar 

Download references

Acknowledgements

This study was funded by the National Natural Science Foundation of China under Grant 61773282. The authors would like to thank the associate editor and reviewers for their valuable comments and suggestions that improved the paper’s quality. Gratitude is extended to Big Data Intelligence Centre of The Hang Seng University of Hong Kong for supporting the research.

Funding

This study was funded by the National Natural Science Foundation of China (Grant Number 61773282).

Author information

Authors and Affiliations

  1. School of Electrical and Information Engineering, Tianjin University, 92 Weijin Road, Nankai District, Tianjin, China

    Na Dong, Meng-die Zhai & Li Zhao

  2. The Department of Supply Chain and Information Management, The Hang Seng University of Hong Kong, Hong Kong, China

    Chun Ho Wu

Authors
  1. Na Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Meng-die Zhai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Li Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chun Ho Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Na Dong.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Research involving human participants and/or animals

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

This article does not contain patient data.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dong, N., Zhai, Md., Zhao, L. et al. Cervical cell classification based on the CART feature selection algorithm. J Ambient Intell Human Comput 12, 1837–1849 (2021). https://doi.org/10.1007/s12652-020-02256-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-020-02256-9

Keywords

Search

Navigation