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IJML 2023 Vol.13(4): 163-172
DOI: 10.18178/ijml.2023.13.4.1146

Automated Segmentation of Cervical Cell Images Using IMBMDCR-Net

Yanjing Ding, Weiwei Yue, and Qinghua Li*

Manuscript received September 19, 2022; revised December 26, 2022; accepted January 10, 2023.

Abstract—Early screening of cervical lesions is of great significance in pathological diagnosis. Owing to the complexity of cell morphological changes and the limitations of medical images, accurate segmentation of cervical cells is still a challenging task. In this paper, an isomorphic multi-branch modulation deformable convolution residual model is proposed to extract features for enhancing the segmentation of small cells and overlapping cytoplasmic boundaries. Then the regional feature extraction, boundary box recognition, and adding a single pixel-level mask at the last level are integrated and optimized based on the cascade regional convolution neural network (Cascade R-CNN) to complete the segmentation of cervical cells for getting better accuracy. The proposed framework was evaluated on the ISBI2014 cervical cell segmentation competition public dataset. Experimental results show that the average accuracy of the network model in cervical cell segmentation is 81.1%, and the accuracy of small targets is 77%. To some extent, it can assist pathologists in screening cervical cancer in the early phase.

Index Terms—Cervical cell, instance segmentation, cascade RCNN, modulation deformable convolution, residual network, deep learning

Yanjing Ding and Weiwei Yue are with School of Physics and Electronics, Shandong Normal University, Jinan, China.
Qinghua Li is with College of Artificial Intelligence and Big Data for Medical Sciences SDFMU, Jinan, China.
*Correspondence: liqinghua1977@163.com (Q.L.)


Cite: Yanjing Ding, Weiwei Yue, and Qinghua Li*, "Automated Segmentation of Cervical Cell Images Using IMBMDCR-Net," International Journal of Machine Learning vol. 13, no. 4, pp. 163-172, 2023.

Copyright @ 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net

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