Skip to main content
SpringerLink
Log in

Cervical cancer classification using sparse stacked autoencoder and fuzzy ARTMAP

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Cervical cancer (CC) is affecting women predominantly, and early diagnosis could cure this cancer. This study aims to design and develop an effective deep learning-based classification model to detect early CC stages using clinical data. The proposed method is a combination of an unsupervised deep learning and a supervised neural network, i.e. sparse stacked autoencoder (SSAE) and fuzzy adaptive resonance theory MAP (FAM), respectively, and is denoted as SSAE-FAM. Specifically, SSAE is applied to tackle the data sparsity problem. It extracts the representative features from a data set through feature transformation. The transformed features are then classified by FAM. In this study, a CC data set obtained from the University of California Irvine (UCI) machine learning repository is utilised for evaluation. Owing to missing data in the original CC data set, two data sets are generated from the original CC data samples using two data preprocessing techniques. Both generated CC data sets with four target classes (i.e. Schiller, Cytology, Biopsy, and Hinselmann) are evaluated as four independent binary-class problems. We improve the classification performance of FAM by mitigating the data sparsity problem. Based on a series of experimental studies, SSAE-FAM outperforms other state-of-art methods by achieving 99.47%, 99.34%, 99.48%, and 99.81% mean accuracy rates, respectively, with the first CC data set, and 99.74%, 99.86%, 99.77%, and 99.80% mean accuracy rates, respectively, with the second CC data set. The results positively indicate the usefulness of SSAE-FAM for early CC diagnosis.

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
Algorithm 1

Similar content being viewed by others

Data availability

The datasets used in this research work are available in http://archive.ics.uci.edu/ml.

References

  1. Ilango B, Nithya V (2019) Evaluation of machine learning based optimized feature selection approaches and classification methods for cervical cancer prediction. SN Appl Sci. https://doi.org/10.1007/s42452-019-0645-7

    Article  Google Scholar 

  2. WHO (2013) Comprehensive cervical cancer prevention and control: a healthier future for girls and woman. Youth and Health Risks, pp 1–12

  3. Marván ML, López-Vázquez E (2017) Preventing health and environmental risks in Latin America. The Anthropocene: Politik-Economics-Society-Science (APESS), 23

  4. Castanon A, Sasieni P (2018) Is the recent increase in cervical cancer in women aged 20–24 years in England a cause for concern? Prev Med 107:21–28

    Article  Google Scholar 

  5. Oluwole EO, Mohammed AS, Akinyinka MR, Salako O (2017) Cervical cancer awareness and screening uptake among rural women in Lagos, Nigeria. J Commun Med Primary Health Care 29(1):81–88

    Google Scholar 

  6. Fernandes K, Cardoso J, Fernandes J (2018) Automated methods for the decision support of cervical cancer screening using digital colposcopies. IEEE Access 6:33910–33927

    Article  Google Scholar 

  7. Cleveland Clinic (2022) Cervical cancer. Retrieved from: https://my.clevelandclinic.org/health/diseases/12216-cervical-cancer

  8. WHO (2023) Cervical cancer. Retrieved from: https://www.who.int/news-room/fact-sheets/detail/cervical-cancer

  9. Chatterjee S, Divesh G, Prakash A, Sharma A (2021) Exploring healthcare/health-product ecommerce satisfaction: a text mining and machine learning application. J Bus Res 131:815–825

    Article  Google Scholar 

  10. Prabhpreet K, Gurvinder S, Parminder K (2019) Intellectual detection and validation of automated mammogram breast cancer images by multi-class SVM using deep learning classification. Inf Med Unlocked 16:100151

    Article  Google Scholar 

  11. Osuwa A, Öztoprak H (2021) Importance of continuous improvement of machine learning algorithms from a health care management and management information systems perspective. In Proceedings of the 2021 International Conference on Engineering and Emerging Technologies (ICEET), pp 1–5

  12. Yan B, Han G (2018) Effective feature extraction via stacked sparse autoencoder to improve intrusion detection system. IEEE Access 6:41238–41248, retrieved from: https://doi.org/10.1109/ACCESS.2018.2858277

  13. Furey TS, Cristianini N, Duffy N, Bednarski DW, Schummer M, Haussler D (2000) Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 16(10):906–914

    Article  Google Scholar 

  14. Carpenter GA, Grossberg S (1987) A massively parallel architecture for a self-organizing neural pattern recognition machine. Comput Vis Graph Image Process 37(1):54–115

    Article  Google Scholar 

  15. Carpenter GA, Grossberg S (1988) The ART of adaptive pattern recognition by a self-organizing neural network. IEEE Comput 21(3):77–88

    Article  Google Scholar 

  16. Zhang Y-D, Zhang Y, Hou X-X, Chen H, Wang S-H (2017) Seven-layer deep neural network based on sparse autoencoder for voxelwise detection of cerebral microbleed. Multimedia Tools Appl 77(9):10521–10538

    Article  Google Scholar 

  17. Jia WJ, Muhammad K, Wang S-H, Zhang Y-D (2017) Five-category classification of pathological brain images based on deep stacked sparse autoencoder. Multimedia Tools Appl 78(4):4045–4064

    Article  Google Scholar 

  18. Zhang Y-D, Khan MA, Zhu ZQ, Wang S-H (2021) Pseudo Zernike moment and deep stacked sparse autoencoder for COVID-19 diagnosis. Comput Mater Contin 69(3):3145–3162

    Google Scholar 

  19. Ho SH, Jee SH, Lee JE, Park JS (2004) Analysis on risk factors for cervical cancer using induction technique. Expert Syst Appl 27(1):97–105

    Article  Google Scholar 

  20. Hair JF, Anderson RE, Tatham RL, Black WC (1998) Multivariate data analysis, 5th edn. Prentice Hall, Upper Saddle River

    Google Scholar 

  21. Tseng CJ, Lu CJ, Chang CC, Chen GD (2014) Application of machine learning to predict the recurrence-proneness for cervical cancer. Neural Comput Appl 24(6):1311–1316

    Article  Google Scholar 

  22. Sharma S (2016) Cervical cancer stage prediction using decision tree approach of machine learning. Int J Adv Res Comput Commun Eng 5(4):345–348

    Google Scholar 

  23. Gomathi M, Thangaraj P (2011) A computer aided diagnosis system for lung cancer detection using machine learning technique. Eur J Sci Res 51:260–275

    Google Scholar 

  24. Malar E, Kandaswamy A, Chakravarthy D, Giri Dharan A (2012) A novel approach for detection and classification of mammographic microcalcifications using wavelet analysis and extreme learning machine. Comput Biol Med 42(9):898–905

    Article  Google Scholar 

  25. Yu SX, Feng XX, Wang B, Dun H, Zhang S, Zhang RH, Huang X (2021) Automatic classification of cervical cells using deep learning method. IEEE Access 9:32559–32568

    Article  Google Scholar 

  26. Ghoneim A, Muhammad G, Hossain MS (2019) Cervical cancer classification using convolutional neural networks and extreme learning machines. Futur Gener Comput Syst 102:643–649

    Article  Google Scholar 

  27. Ali M, Ahmed K, Bui FM, Paul BK, Ibrahim SM, Quinn JMW, Moni MA (2021) Machine learning-based statistical analysis for early stage detection of cervical cancer. Comput Biol Med 139:104985

    Article  Google Scholar 

  28. Cover T, Hart P (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13(1):21–27

    Article  Google Scholar 

  29. Cleary JG, Trigg LE (1995) K*: an instance-based learner using an entropic distance measure. In: Proceedings of the 12th international conference on machine learning, pp 108–114

  30. Yates, D., Islam, M. Z., & Gao J. (2018). SPAARC: a fast decision tree algorithm. In: Australasian conference on data mining, pp 43–55

  31. Kalmegh S (2015) Analysis of WEKA data mining algorithm REPTree, simple cart and RrandomTtree for classification of Indian news. Int J Innov Sci Eng Technol 2(2):438–446

    Google Scholar 

  32. Breiman L (2001) Random forests. Mach Learn 45:5–32

    Article  Google Scholar 

  33. 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 

  34. Dua D, Graff C (2019) UCI Machine Learning Repository, retrieved from: http://archive.ics.uci.edu/ml

  35. Newaz A, Muhtadi S, Haq FS (2022) An intelligent decision support system for the accurate diagnosis of cervical cancer. Knowl Based Syst 245:108634

    Article  Google Scholar 

  36. Chaudhuri AK, Ray A, Banerjee DK, Das A (2021) A multi-stage approach combining feature selection with machine learning techniques for higher prediction reliability and accuracy in cervical cancer diagnosis. Int J Intell Syst Appl 5:46–63

    Google Scholar 

  37. Dweekat OY, Lam SS (2022) Cervical cancer diagnosis using an integrated system of principal component analysis, genetic algorithm, and multilayer perceptron. Healthcare, 10(10), retrieved from: https://doi.org/10.3390/healthcare10102002

  38. Adem K, Kiliçarslan S, Cömert O (2019) Classification and diagnosis of cervical cancer with stacked autoencoder and softmax classification. Expert Syst Appl 115:557–564

    Article  Google Scholar 

  39. Tanimu JJ, Hamada M, Hassan M, Kakudi H, Abiodun JO (2022) A machine learning method for classification of cervical cancer. Electronics 11(3):463

    Article  Google Scholar 

  40. Sun G, Li S, Cao Y, Lang F (2017) Cervical cancer diagnosis based on random forest. Int J Perform Eng 13:446–457

    Google Scholar 

  41. Khamparia A, Gupta D, Rodrigues JJPC, de Albuquerque VHC (2021) DCAVN: Cervical cancer prediction and classification using deep convolutional and variational autoencoder network. Multimedia Tools Appl 80(1):30399–30415

    Article  Google Scholar 

  42. Jović A, Brkić K, Bogunović N (2015) A review of feature selection methods with applications. In: 2015 38th international convention on information and communication technology, electronics and microelectronics (MIPRO), 1200–1205, retrieved from: https://doi.org/10.1109/MIPRO.2015.7160458.

  43. Chandrashekar G, Sahin F (2014) A survey on feature selection methods. Comput Electr Eng 40(1):16–28

    Article  Google Scholar 

  44. Tang J, Alelyani S, Liu H (2014) Feature selection for classification: a review. Data classification: algorithms and applications. CRC Press, Boca Raton

    Google Scholar 

  45. Ng A (2011) Sparse autoencoder. CS294A Lecture Notes 72:1–19

  46. Rumelhart DE, Hinton GE, Williams RJ (1986) Learning internal representations by error propagation. Parallel Distrib Process Explor Microstruct Cogn 1:318–362

    Google Scholar 

  47. Baştürk A, Yüksei ME, Badem H, Çalışkan A (2017) Deep neural network based diagnosis system for melanoma skin cancer. In: 25th signal processing and communications applications conference (SIU), pp 1–4

  48. Kaynar O, Yüksek AG, Görmez Y, Işik YE (2017) Intrusion detection with autoencoder based deep learning machine. In: 25th signal processing and communications applications conference (SIU), pp 1–4

  49. Tyagi K, Rane C, Harshvardhan, Manry M (2022) Chapter 4—Regression analysis. Artificial intelligence and machine learning for EDGE computing, Academic Press, pp 53–63

  50. Olshausenand BA, Field DJ (1996) Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381(6583):607–609

    Article  Google Scholar 

  51. Bengio Y, Lamblin P, Popovici D, Larochelle H (2007) Greedy layer-wise training of deep networks. In: Proceedings of the 21st international conference on neural information processing systems (NIPS), pp 153–160

  52. Singh P, Singh S, Pandi-Jain GS (2018) Effective heart disease prediction system using data mining techniques. Int J Nanomed 13:121–124

    Article  Google Scholar 

  53. Mohan S, Thirumalai C, Srivastava G (2019) Effective heart disease prediction using hybrid machine learning techniques. IEEE Access 7:81542–81554

    Article  Google Scholar 

  54. Thabtah F, Peebles D (2020) A new machine learning model based on induction of rules for autism detection. Health Inform J 26(1):264–286

    Article  Google Scholar 

  55. Fix E, Hodges JL (1951) Discriminatory analysis, nonparametric discrimination: consistency properties. Int Stat Rev 57(3):238–247

    Article  Google Scholar 

  56. Quinlan JR (1986) Induction of decision trees. Mach Learn 1:81–106

    Article  Google Scholar 

  57. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297

    Article  Google Scholar 

  58. Rosenblatt F (1958) The perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev 65(6):386–408

    Article  Google Scholar 

  59. Rodríguez JJ, Kuncheva LI, Alonso CJ (2006) Rotation forest: a new classifier ensemble method. IEEE Trans Pattern Anal Mach Intell 28(10):1619–1630

    Article  Google Scholar 

Download references

Acknowledgements

This research is supported by Fundamental Research Grant Scheme (FRGS), FRGS/1/2019/ICT02/MMU/02/2.

Author information

Authors and Affiliations

  1. Faculty of Information Science and Technology, Multimedia University, 75450, Melaka, Malaysia

    Lawrence Chuin Ming Liaw, Shing Chiang Tan & Pey Yun Goh

  2. Institute for Intelligent Systems Research and Innovation, Deakin University, Geelong Waurn Ponds, VIC, 3216, Australia

    Chee Peng Lim

Authors
  1. Lawrence Chuin Ming Liaw
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shing Chiang Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pey Yun Goh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chee Peng Lim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shing Chiang Tan.

Ethics declarations

Conflict of interest

The authors declare that they have no competing interests.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liaw, L.C.M., Tan, S.C., Goh, P.Y. et al. Cervical cancer classification using sparse stacked autoencoder and fuzzy ARTMAP. Neural Comput & Applic (2024). https://doi.org/10.1007/s00521-024-09706-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00521-024-09706-x

Keywords

Search

Navigation