基于YOLO模型的宫颈细胞簇团智能识别方法

郑欣; 田博; 李晶晶

doi:10.3788/YJYXS20183311.0965

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基于YOLO模型的宫颈细胞簇团智能识别方法

图像处理 | 更新时间：2020-08-12

- 基于YOLO模型的宫颈细胞簇团智能识别方法
- Intelligent recognition method of cervical cell cluster based on YOLO model
- 液晶与显示 2018年33卷第11期页码：965-971
- 作者机构：
  
  1.电子科技大学计算机科学与工程学院, 四川成都 611731
  2.成都九鼎天元知识产权代理有限公司, 四川成都 610041
  3.江苏科技大学计算机科学与工程学院, 江苏镇江 212003
- 作者简介：
  
  [ "郑欣(1981-), 男, 四川绵阳人, 博士后; 研究方向:深度学习、机器学习技术在医疗及大健康领域的应用。E-mail:1820745320@qq.com" ]
  [ "田博(1982-), 男, 硕士, 国家知识产权管理体系审核员, 专利代理人; 研究方向:企业在人工智能领域的专利战略规划、前瞻性布局, 专利分析及挖掘; E-mail: 1908528954@qq.com" ]
  [ "李晶晶(1985-), 女, 硕士, 研究方向:深度学习、机器学习技术在医疗及大健康领域的应用; E-mail: ljj121607@163.com" ]
- 基金信息：
  
  国家自然科学基金面上项目(61775030);中国科学院光束控制重点实验室基金(2017LBC003);广东省应用型研发重大专项基金(2015BD10131002);National Natural Science Foundation of China(61775030);Light Control Key Laboratory Open Fund of the Chinese Academy of Sciences(2017LBC003);The Important Special Fund for Applied R & D in Guangdong Province(2015BD10131002)
- DOI：10.3788/YJYXS20183311.0965
  中图分类号： TP394.1;TH691.9
- 收稿日期：2018-08-02，
  
  录用日期：2018-8-29，
  
  纸质出版日期：2018-11-05
- 稿件说明：
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郑欣, 田博, 李晶晶. 基于YOLO模型的宫颈细胞簇团智能识别方法[J]. 液晶与显示, 2018,33(11):965-971.

Xin ZHENG, Bo TIAN, Jing-jing LI. Intelligent recognition method of cervical cell cluster based on YOLO model[J]. Chinese journal of liquid crystals and displays, 2018, 33(11): 965-971.
郑欣, 田博, 李晶晶. 基于YOLO模型的宫颈细胞簇团智能识别方法[J]. 液晶与显示, 2018,33(11):965-971. DOI： 10.3788/YJYXS20183311.0965.

Xin ZHENG, Bo TIAN, Jing-jing LI. Intelligent recognition method of cervical cell cluster based on YOLO model[J]. Chinese journal of liquid crystals and displays, 2018, 33(11): 965-971. DOI： 10.3788/YJYXS20183311.0965.

摘要

针对宫颈细胞簇团自动识别问题，本文提出了一种基于YOLO v2模型的智能识别方法。首先，针对宫颈细胞簇团识别任务的特点，采用resnet 50模型作为YOLO v2网络的基础特征提取模块。同时，提出了相应的数据扩增方法与YOLO v2网络的训练方案。同时，我们收集宫颈细胞液基涂片扫描图像，建立了宫颈细胞簇团图像数据集，并由细胞病理专家对其中的细胞簇团进行了标注。实验表明，本文方法能够有效完成宫颈细胞病变簇团的自动识别，在测试图像集中，针对细胞簇团识别的准确率为75.9%，召回率为86.3%；针对宫颈细胞图像识别的准确率为87.0%，召回率为86.7%。本文将深度学习技术引入到宫颈细胞辅助筛查领域，对于促进宫颈癌早期自动筛查系统的研究，具有重要意义。

Abstract

Aiming to the automatic recognition of cervical cell cluster

a smart recognition method based on YOLO v2 model was proposed. At first

the model resnet50 was used as basic feature extraction module according to the characters of cervical cell cluster recognition task. Meanwhile

the related data amplification and training program of YOLO v2 network were also proposed. At the same time

we collect the scan image of cervical cell liquid base smear to build the cervical cell cluster image data set and the cell cluster was marked by cytopathic experts. The result shows that the automatic recognition of cervical cell cluster was effectively realized with this method. The accuracy rate of cervical cell cluster was 75.9% and the recall rate was 86.3%. The accuracy rate of cervical cell pathological identification was 87.0% and the recall rate was 86.7%. In this paper

deep learning technique was leaded into the cervical cell auxiliary screening field

it can promote the research of automatic auxiliary screening of early cervical cancer.

关键词

Keywords

references

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