174
Views
4
CrossRef citations to date
0
Altmetric
Articles

Cervical Cancer Classification from Pap Smear Images Using Modified Fuzzy C Means, PCA, and KNN

ORCID Icon & ORCID Icon

References

  • A. Sreedevi, R. Javed, and A. Dinesh, “Epidemiology of cervical cancer with special focus on India,” Int. J. Women's Health, Vol. 7, pp. 405–414, Apr. 2015.
  • R. Landy, F. Pesola, A. Castanon, and P. Sasieni, “Impact of cervical screening on cervical cancer mortality: estimation using stage-specific results from a nested case–control study,” Br. J. Cancer, Vol. 115, no. 9, pp. 1140–1146, Sep. 2016. doi:10.1038/bjc.2016.290.
  • G. M. Ginsberga, T. T.-T. Edejera, J. A. Lauera, and C. Sepulvedab, “Screening, prevention and treatment of cervical cancer—A global and regional generalized cost-effectiveness analysis,” Vaccine, Vol. 27, no. 43, pp. 6060–6079, Oct. 2009.
  • G. G. Birdsong, “Automated screening of cervical cytology specimens,” Human Pathol, Vol. 27, no. 5, pp. 468–481, 1996.
  • Y. Zhang, S. Lu, X. Zhou, M. Yang, L. Wu, B. Liu, and S. Wang, “Comparison of machine learning methods for stationary wavelet entropy-based multiple sclerosis detection: decision tree, k-nearest neighbors, and support vector machine. Simulation, Vol. 92, no. 9, pp. 861–871, 2016.
  • Y. D. Zhang, and L. Wu, “An MR brain images classifier via principal component analysis and kernel support vector machine,” Prog. Electromagn. Res., Vol. 130, pp. 369–388, 2012.
  • F. Ozyurt, T. Tuncer, and A. Subasi, “An automated COVID-19 detection based on fused dynamic exemplar pyramid feature extraction and hybrid feature selection using deep learning,” Comput. Biol. Med., Vol. 132, pp. 104356, 2021.
  • T. Tuncer, F. Ozyurt, S. Dogan, and A. Subasi, “A novel Covid-19 and pneumonia classification method based on F-transform,” Chemom. Intell. Lab. Syst., Vol. 210, pp. 104256, 2021.
  • A. R. Bhatt, A. Ganatra, and K. Kotecha, “Cervical cancer detection in pap smear whole slide images using convNet with transfer learning and progressive resizing,” PeerJ Comput. Sci., Vol. 7, pp. e348, 2021.
  • M. A. Devi, S. Ravi, J. Vaishnavi, and S. Punitha, “Classification of cervical cancer using artificial neural networks,” Procedia. Comput. Sci., Vol. 89, pp. 465–472, 2016.
  • M. Arya, N. Mittal, and G. Singh, “Clustering techniques on pap smear images for the detection of cervical cancer,” J Biol Today’s World, Vol. 7, no. 1, pp. 30–35, 2018.
  • W. William, A. Ware, A. H. Basaza-Ejiri, and J. Obungoloch, “Cervical cancer classification from Pap-smears using an enhanced fuzzy C-means algorithm,” Inform. Med. Unlocked, Vol. 14, pp. 23–33, 2019.
  • J. Liu, Y. Peng, and Y. Zhang, “A fuzzy reasoning model for cervical intraepithelial neoplasia classification using temporal grayscale change and textures of cervical images during acetic acid tests,” IEEE. Access., Vol. 7, pp. 13536–13545, 2019.
  • A. Tareef, Y. Song, H. Huang, D. Feng, M. Chen, Y. Wang, and M. Weidong Cai, “Multi-Pass fast watershed for accurate segmentation of overlapping cervical cells,” IEEE Trans. Med. Imaging, Vol. 37, no. 9, pp. 2044–2059, 2018.
  • S.-F. Yang-Mao, Y.-K. Chan, and Y.-P. Chu, “Edge enhancement nucleus and cytoplast contour detector of cervical smear images,” IEEE Trans. Syst. Man Cybern. B Cybern, Vol. 38, no. 2, pp. 353–366, Apr. 2008.
  • M. Arya, N. Mittal, and G. Singh, “Texture-based feature extraction of smear images for the detection of cervical cancer,” IET Comput. Vision, Vol. 12, no. 8, pp. 1049–1059, Sep. 2018. doi:10.1049/iet-cvi.2018.5349.
  • M. N. Asiedu, et al., “Development of algorithms for automated detection of cervical Pre-cancers with a low-cost, point-of-care, pocket colposcope,” IEEE Trans. Biomed. Eng., Vol. 66, no. 8, pp. 2306–2318, 2019.
  • S. Fayz, M. A. Rizka, and F. Maghraby, “Cervical cancer diagnosis using random forest classifier with SMOTE and feature reduction techniques,” IEEE. Access., Vol. 6, pp. 59475–59485, Oct. 2018.
  • Q. Zhang, Y. Liu, H. Han, J. Shi1, and W. Wang, “Artificial intelligence based diagnosis for cervical lymph node malignancy using the point-wise gated Boltzmann machine,” IEEE. Access., Vol. 6, pp. 60605–60612, 2018.
  • M. Veluchamy, K. Perumal, and T. Ponuchamy, “Feature extraction and classification of blood cells using artificial neural network,” Amer. J. Appl. Sci., Vol. 9, no. 5, pp. 615–619, Jun. 2015.
  • P. Thirumurugan, and N. Lavanya Devi, “Automated detection of cervical cancer,” Int. J. Innov. Technol. Exploring Engineering, Vol. 8, no. 10, pp. 2278–3075, Aug. 2019.
  • J. Hephzipah, and P. Thirumurugan, “Performance analysis of meningioma brain tumor detection system using feature learning optimization and ANFIS classification method,” IETE. J. Res., 1–9, 2020. doi:10.1080/03772063.2020.1844079.
  • W. Wu, and H. Zhou, “Data-driven diagnosis of cervical cancer with support vector machine-based approaches,” IEEE. Access., Vol. 5, pp. 25189–25195, 2017.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.