ARCHIVES
Research Article
Segmentation and Classification of Cervical Cancer Cells
S.KRITHIGA1
V. GUNAPRIYA2
C.J EYAGOHILA3
E.ARCHANAA4
A.THAMARAISELVI5
1234Student, Department of computer science and engineering, Vivekananda College of Engineering for women, Tiruchengode , Namakkal District, Tamil Nadu, 637205, India. 5Assistant professor, Department of computer science and engineering, Vivekananda College of Engineering for women, Tiruchengode, Namakkal District, Tamil Nadu,637205,India.
Published Online: May-June 2022
Pages: 358-362
Cite this article
No DOIReferences
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3. C. Li, D. Xue, X. Zhou, J. Zhang, H. Zhang, Y.Yao, F. Kong, L. Zhang, and H. Sun,
4. ``Transfer learning based classi_cation of cervical cancer immunohistochemistryimages,'' in Proc. 3rd Int. Symp. Image Comput. Digit. Med. (ISICDM), 2019, pp. 102_106.
5. C. Li, H. Chen, L. Zhang, N. Xu, D. Xue, Z. Hu, H. Ma, and H. Sun,``Cervical histopathology image classi_cation using multilayer hidden conditional random _elds and weakly supervised learning,'' IEEE Access,vol. 7, pp. 90378_90397, 2019.
6. B. Zhang, S. Qi, P. Monkam, C. Li, F. Yang, Y.-D. Yao, and W. Qian,`Ensemble learners of multiple deep CNNs for pulmonary nodules
classi_cation using CT images,'' IEEE Access, vol. 7, pp. 110358_110371,2019
7. Gautam, H. K. K., N. Jith, A. K. Sao, A. Bhavsar, and A. Natarajan, ``Considerations for a PAP smear image analysis system with CNN features,'' 2018, arXiv:1806.09025. [Online]. Available: http://arxiv.org/abs/1806.09025
8. N. Crossley, C. Tipton, T. Meier, M. Sudhoff, and J. Kharofa, ``The value of hybrid interstitial tandem and ring applicators for organ at risk dose reduction in small volume cervical cancer,'' Brachytherapy, vol. 17, no. 4, p. S111, Jul. 2018
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11. 10 H. Komagata, T. Ichimura, Y. Matsuta, M. Ishikawa, K. Shinoda, N. Kobayashi, and A. Sasaki, ‘‘Feature analysis of cell nuclear chromatin distribution in support of cervicalcytology,’’ J. Med. Imag., vol. 4, no. 4, p. 1, Oct. 2017.
12. L. Wei, Q. Gan, and T. Ji, ‘‘Cervical cancer histology image identification method based ontexture and lesion area features,’’ Comput. Assist. Surg., vol. 22, no. 1, pp. 186–199, Oct. 2017
13. M. M. Ghazi, B. Yanikoglu, and E. Aptoula, ‘‘Plant identification using deep neural networks via optimization of transfer learning parameters,’’ Neurocomputing, vol. 235, pp. 228–235, Apr. 2017.
14. F. Shoeleh and M. Asadpour, ‘‘Graph based skill acquisition and transfer learning for continuous reinforcement learning domains,’’ Pattern Recognit. Lett., vol. 87, pp. 104–116, Feb. 2017
machine vision approaches,'' Artif. Intell. Rev., pp. 1_42, Feb. 2020, doi:10.1007/s10462-020-09808-7.
2. M. Rahaman, C. Li, X. Wu, Y. Yao, Z. Hu, T. Jiang, X. Li, and S. Qi,``A survey for cervical cytopathology image analysis using deep
learning,''IEEE Access, vol. 8, no. 1, pp.61687_61710, 2020.
3. C. Li, D. Xue, X. Zhou, J. Zhang, H. Zhang, Y.Yao, F. Kong, L. Zhang, and H. Sun,
4. ``Transfer learning based classi_cation of cervical cancer immunohistochemistryimages,'' in Proc. 3rd Int. Symp. Image Comput. Digit. Med. (ISICDM), 2019, pp. 102_106.
5. C. Li, H. Chen, L. Zhang, N. Xu, D. Xue, Z. Hu, H. Ma, and H. Sun,``Cervical histopathology image classi_cation using multilayer hidden conditional random _elds and weakly supervised learning,'' IEEE Access,vol. 7, pp. 90378_90397, 2019.
6. B. Zhang, S. Qi, P. Monkam, C. Li, F. Yang, Y.-D. Yao, and W. Qian,`Ensemble learners of multiple deep CNNs for pulmonary nodules
classi_cation using CT images,'' IEEE Access, vol. 7, pp. 110358_110371,2019
7. Gautam, H. K. K., N. Jith, A. K. Sao, A. Bhavsar, and A. Natarajan, ``Considerations for a PAP smear image analysis system with CNN features,'' 2018, arXiv:1806.09025. [Online]. Available: http://arxiv.org/abs/1806.09025
8. N. Crossley, C. Tipton, T. Meier, M. Sudhoff, and J. Kharofa, ``The value of hybrid interstitial tandem and ring applicators for organ at risk dose reduction in small volume cervical cancer,'' Brachytherapy, vol. 17, no. 4, p. S111, Jul. 2018
9. C. Qian, Y. Yu, and Z.-H. Zhou, ``Analyzing evolutionary optimization in noisyenvironments,'' Evol. Comput., vol. 26, no. 1, pp. 1_41, Mar.2018.J.
10. . A. Jothi and V. M. A. Rajam, ‘‘A survey on automated cancer diagnosis from histopathology images,’’ Artif. Intell. Rev., vol. 48, no. 1, pp. 31–81, Jun. 2017.
11. 10 H. Komagata, T. Ichimura, Y. Matsuta, M. Ishikawa, K. Shinoda, N. Kobayashi, and A. Sasaki, ‘‘Feature analysis of cell nuclear chromatin distribution in support of cervicalcytology,’’ J. Med. Imag., vol. 4, no. 4, p. 1, Oct. 2017.
12. L. Wei, Q. Gan, and T. Ji, ‘‘Cervical cancer histology image identification method based ontexture and lesion area features,’’ Comput. Assist. Surg., vol. 22, no. 1, pp. 186–199, Oct. 2017
13. M. M. Ghazi, B. Yanikoglu, and E. Aptoula, ‘‘Plant identification using deep neural networks via optimization of transfer learning parameters,’’ Neurocomputing, vol. 235, pp. 228–235, Apr. 2017.
14. F. Shoeleh and M. Asadpour, ‘‘Graph based skill acquisition and transfer learning for continuous reinforcement learning domains,’’ Pattern Recognit. Lett., vol. 87, pp. 104–116, Feb. 2017
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