Zengyong Xu1 and Meili Rao This email address is being protected from spambots. You need JavaScript enabled to view it.2

1Henan College of Transportation, Zhengzhou 450000, China
2School of Electrical Engineering, Zhengzhou University of Science and Technology, Zhengzhou 450000, China


 

Received: September 21, 2020
Accepted: October 24, 2020
Publication Date: April 1, 2021

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.6180/jase.202104_24(2).0010  


ABSTRACT


In the cervical cancer cell image recognition, there are some problems such as insufficient labeled data, single scale feature and poor robustness, the recognition rate is low. Therefore, we propose a multi-scale multi-feature convolutional neural network (M2CNN) method for cervical cancer cell recognition. Firstly, we conduct data enhancement due to the limited cervical cancer data set to avoid over-fitting. The image is rotated to three different scales using the Laplacian pyramid method. In order to enhance the feature robustness, the above three scales are used as the inputs of the model. The model also learns the features of the three scales. The weighted sum of multi-scale information in different layers of the network makes the features of different layers have different effects on the final result to improve the robustness of the model. Experimental results show that this method can effectively complete the automatic recognition of cervical cancer cells. In terms of the accuracy and recall rate, our proposed method greatly improves the results. In this paper, deep learning technology is introduced into the field of cervical cell-assisted screening, which is of great significance for promoting the research of early automatic screening for cervical cancer.


Keywords: Cervical cancer cell recognition, M2CNN, Laplacian pyramid method, data enhancement


REFERENCES


  1. [1] John Brodersen, Volkert Siersma, and Hanne Thorsen. Consequences of screening in cervical cancer: Development and dimensionality of a questionnaire. BMC Psychology, 6(1):39, aug 2018.
  2. [2] Masateru Fujiwara, Fumiaki Isohashi, Seiji Mabuchi, Yasuo Yoshioka, Yuji Seo, Osamu Suzuki, Iori Sumida, Kazuhiko Hayashi, Tadashi Kimura, and Kazuhiko Ogawa. Efficacy and safety of nedaplatin-based concurrent chemoradiotherapy for FIGO Stage IB2-IVA cervical cancer and its clinical prognostic factors. In Journal of Radiation Research, volume 56, pages 305–314. Oxford University Press, 2015.
  3. [3] D. R. Thangavelu. Age is not a limiting factor for Cervical Cancer Screening. BMJ, 338, 2020.
  4. [4] Shoulin Yin, Hang Li, Desheng Liu, and Shahid Karim. Active contour modal based on densityoriented BIRCH clustering method for medical image segmentation. Multimedia Tools and Applications, 79(41- 42):31049–31068, nov 2020.
  5. [5] Shoulin Yin, Ye Zhang, and Shahid Karim. Large Scale Remote Sensing Image Segmentation Based on Fuzzy Region Competition and Gaussian Mixture Model. IEEE Access, 6:26069–26080, may 2018.
  6. [6] Lin Teng, Hang Li, Shoulin Yin, and Yang Sun. Improved krill group-based region growing algorithm for image segmentation, oct 2019.
  7. [7] Yuanyuan Wang, Li Chen, Fengying Zhu, Wanjing Guo, Dandan Zhang, and Wenzhao Sun. A study of minimum segment width parameter on VMAT plan quality, delivery accuracy, and efficiency for cervical cancer using Monaco TPS. Journal of Applied Clinical Medical Physics, 19(5):609–615, sep 2018.
  8. [8] Sherif F. Abdoh, Mohamed Abo Rizka, and Fahima A. Maghraby. Cervical cancer diagnosis using random forest classifier with SMOTE and feature reduction techniques. IEEE Access, 6:59475–59485, 2018.
  9. [9] Q. Ningjia, Z. Wen, and et al Peng W. Research on feature selection algorithm combined with improved CHI and RFFS. Computer Engineering and Applications, 2018.
  10. [10] Masoud S Nosrati and Ghassan Hamarneh. Segmentation of overlapping cervical cells: A variational method with star-shape prior. In Proceedings - International Symposium on Biomedical Imaging, volume 2015-July, pages 186–189, 2015.
  11. [11] Daniela Ushizima, Andrea G. C. Bianchi, and Claudia M. Carneiro. Segmentation of subcellular compartments combining superpixel representation with Voronoi diagrams This work was partially supported by the Office of Energy Research , U . S . Department of Energy , under Contract Number DE-AC02-05CH11231 . pages 5–7, jan 2015.
  12. [12] Zhi Lu, Gustavo Carneiro, and Andrew P. Bradley. An improved joint optimization of multiple level set functions for the segmentation of overlapping cervical cells. IEEE Transactions on Image Processing, 24(4):1261– 1272, apr 2015.
  13. [13] Hady Ahmady Phoulady, Dmitry B. Goldgof, Lawrence O. Hall, and Peter R. Mouton. A new approach to detect and segment overlapping cells in multi-layer cervical cell volume images. Proceedings - International Symposium on Biomedical Imaging, 2016- June(Section 4):201–204, 2016.
  14. [14] Lin Teng, Hang Li, and Shahid Karim. DMCNN: A Deep Multiscale Convolutional Neural Network Model for Medical Image Segmentation. Journal of Healthcare Engineering, 2019.
  15. [15] Shoulin Yin and Jing Bi. Medical image annotation based on deep transfer learning. Journal of Applied Science and Engineering, 22(2):385–390, 2019.
  16. [16] Lin Teng, Hang Li, Shoulin Yin, Shahid Karim, and Yang Sun. An active contour model based on hybrid energy and fisher criterion for image segmentation, jan 2020.
  17. [17] Peng Li, Zhikui Chen, Laurence Tianruo Yang, Jing Gao, Qingchen Zhang, and M. Jamal Deen. An Incremental Deep Convolutional Computation Model for Feature Learning on Industrial Big Data. IEEE Transactions on Industrial Informatics, 15(3):1341–1349, mar 2019.
  18. [18] Hongzhu Wang, Chuan Jiang, Kunzhong Bao, and Caie Xu. Recognition and Clinical Diagnosis of Cervical Cancer Cells Based on our Improved Lightweight Deep Network for Pathological Image. Journal of Medical Systems, 43(9), sep 2019.
  19. [19] A. Dongyao Jia, B. Zhengyi Li, and C. Chuanwang Zhang. Detection of cervical cancer cells based on strong feature CNN-SVM network. Neurocomputing, 411:112–127, oct 2020.
  20. [20] Wei Shi, Yuezheng Wang, Lei Hou, Cheng Ma, Lei Yang, Chengang Dong, Zhiquan Wang, Haiqing Wang, Juan Guo, Shenglong Xu, and Jing Li. Detection of living cervical cancer cells by transient terahertz spectroscopy. Journal of Biophotonics, oct 2020.


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