Research on ARS-CNN algorithm in the identification of COVID-19

San-li YI; Tian-wei WANG; Xue-lian YANG; Fu-rong SHE

doi:10.37188/CJLCD.2021-0027

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Research on ARS-CNN algorithm in the identification of COVID-19

Target Identification and Imaging | 更新时间：2021-11-08

- Research on ARS-CNN algorithm in the identification of COVID-19
- Chinese Journal of Liquid Crystals and Displays Vol. 36, Issue 11, Pages: 1565-1572(2021)
- 作者机构：
  
  1.昆明理工大学信息工程与自动化学院, 云南昆明 650500
  2.云南省计算机技术应用重点实验室, 云南昆明 650500
- 作者简介：
- 基金信息：
  
  Supported by National Natural Science Foundation of China(82060329);Yunnan Provincial Department of Education Project(2020J0052)
- DOI：10.37188/CJLCD.2021-0027
  CLC： TP391.41
- Received：27 January 2021，
  
  Revised：28 March 2021，
  
  Published：2021-11
- 稿件说明：
移动端阅览
San-li YI, Tian-wei WANG, Xue-lian YANG, et al. Research on ARS-CNN algorithm in the identification of COVID-19[J]. Chinese journal of liquid crystals and displays, 2021, 36(11): 1565-1572.
DOI：

San-li YI, Tian-wei WANG, Xue-lian YANG, et al. Research on ARS-CNN algorithm in the identification of COVID-19[J]. Chinese journal of liquid crystals and displays, 2021, 36(11): 1565-1572. DOI： 10.37188/CJLCD.2021-0027.

摘要

随着新冠肺炎的蔓延，为了准确诊断新冠肺炎，本文提出了一种改进的基于卷积神经网络的新冠肺炎识别算法即ARS-CNN算法。该算法在CNN网络结构的基础上，加入了新的功能模块：首先，为了捕获不同感受野的多尺度特征信息并加强网络对图像特征的利用，提出了跳跃连接RFB结构；其次，通过短连接aspp模块来改善网络在特征提取过程中图像分辨率减少所导致的局部信息丢失的问题；最后，将注意力机制GC模块与sSE模块进行融合实现对特征信息的筛选并完成特征信息之间的交互，从而提高新冠肺炎识别精度。在公开的COVID-19胸部X光数据集（Chest X-ray Database）上的实验表明，本文所提出的算法的加权平均准确率、精准率、召回率、FI分数、特异性分别为98.22%、97.91%、97.95%、97.92%、98.33%。与其他分类算法相比，本文所提出算法能够对肺部疾病进行高效识别，具有更高的识别性能。

Abstract

With the spread of new coronary pneumonia

in order to accurately diagnose COVID-19

this article proposes an improved new coronary pneumonia recognition algorithm based on convolutional neural network

namely the ARS-CNN algorithm. Based on the CNN network structure

this algorithm adds new functional modules. Firstly

in order to capture the multi-scale feature information of different receptive fields and strengthen the network's use of image features

a jump connection RFB structure is proposed. Secondly

the problem of local information loss caused by the reduction of image resolution during the feature extraction process of the network is improved by short-connecting the aspp module. Finally

the attention mechanism GC module and the sSE module are merged to achieve the screening of feature information and the interaction between feature information

thereby improving the accuracy of new coronary pneumonia recognition. Experiments on the public COVID-19 Chest X-ray Database data set show that the weighted average accuracy

precision

recall

FI score

and specificity of the algorithm proposed in this article are 98.22%

97.91%

97.95%

97.92%

98.33%

respectively. Compared with other classification algorithms

the algorithm proposed in this paper can efficiently recognize lung diseases and has higher recognition performance.

关键词

Keywords

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