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  


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


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