计算机科学与探索 ›› 2022, Vol. 16 ›› Issue (9): 2108-2120.DOI: 10.3778/j.issn.1673-9418.2105117

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融合Grad-CAM和卷积神经网络的COVID-19检测算法

朱炳宇, 刘朕, 张景祥()   

  1. 江南大学 理学院,江苏 无锡 214122
  • 收稿日期:2021-05-11 修回日期:2021-07-15 出版日期:2022-09-01 发布日期:2021-07-23
  • 通讯作者: + E-mail: zhangjingxiang@jiangnan.edu.cn
  • 作者简介:朱炳宇(1997—),男,河北邯郸人,硕士研究生,主要研究方向为人工智能、模式识别、智能计算及应用。
    刘朕(1998—),男,安徽合肥人,硕士研究生,主要研究方向为人工智能、模式识别、智能计算及应用。
    张景祥(1977—),男,吉林通化人,博士,副教授,硕士生导师,主要研究方向为人工智能、模式识别、智能计算及应用。
  • 基金资助:
    国家自然科学基金(61772239);国家自然科学基金(11804123)

COVID-19 Detection Algorithm Combining Grad-CAM and Convolutional Neural Network

ZHU Bingyu, LIU Zhen, ZHANG Jingxiang()   

  1. School of Science, Jiangnan University, Wuxi, Jiangsu 214122, China
  • Received:2021-05-11 Revised:2021-07-15 Online:2022-09-01 Published:2021-07-23
  • About author:ZHU Bingyu, born in 1997, M.S. candidate. His research interests include artificial intelligence, pattern recognition, intelligent computation and its application.
    LIU Zhen, born in 1998, M.S. candidate. His research interests include artificial intelligence, pattern recognition, intelligent computation and its application.
    ZHANG Jingxiang, born in 1977, Ph.D., associate professor, M.S. supervisor. His research interests include artificial intelligence, pattern recognition, intelligent computation and its application.
  • Supported by:
    National Natural Science Foundation of China(61772239);National Natural Science Foundation of China(11804123)

摘要:

新型冠状病毒肺炎(COVID-19)检测中胸部X射线(CXR图像)和电子计算机断层扫描(CT)图像是两种主要技术手段,为医生诊断提供了重要依据。针对当前卷积神经网络(CNN)在医学放射性图像中检测COVID-19的准确率不高、算法复杂、无法标记特征区域的问题,提出了一种融合梯度加权类激活映射(Grad-CAM)颜色可视化和卷积神经网络的算法(GCCV-CNN),对COVID-19阳性患者、COVID-19阴性患者、普通肺炎患者以及正常人的肺部CXR图像和CT扫描图像进行快速分类。通过定位到CXR图像和CT扫描图像中CNN进行分类的关键区域,再综合深度学习算法得到更准确的检测结果。为验证GCCV-CNN算法的有效性,分别在3个COVID-19阳性患者数据集上进行实验,并与已有算法进行比较。结果表明该算法对COVID-19阳性患者的CXR图像和CT扫描图像分类性能优于“新冠网络”(COVID-Net)算法及迁移学习新冠网络(DeTraC-Net)算法,准确率最高达98.06%,速度更快的同时还具有较好的鲁棒性。

关键词: CXR图像, CT扫描图像, COVID-19, Grad-CAM, 融合Grad-CAM颜色可视化和CNN的算法(GCCV-CNN)

Abstract:

In the detection of COVID-19, chest X-ray (CXR) images and CT scan images are two main technical methods, which provide an important basis for doctors' diagnosis. Currently, convolutional neural network (CNN) in detecting the COVID-19 medical radioactive images has problems of low accuracy, complex algorithms, and inability to mark feature regions. In order to solve these problems, this paper proposes an algorithm combining Grad-CAM color visualization and convolutional neural network (GCCV-CNN). The algorithm can quickly classify lung X-ray images and CT scan images of COVID-19-positive patients, COVID-19-negative patients, general pneumonia patients and healthy people. At the same time, it can quickly locate the critical area in X-ray images and CT images. Finally, the algorithm can get more accurate detection results through the synthesis of deep learning algorithms. In order to verify the effectiveness of the GCCV-CNN algorithm, experiments are performed on three COVID-19-positive patient datasets and it is compared with existing algorithms. The results show that the classification performance of the algorithm is better than the COVID-Net algorithm and the DeTraC-Net algorithm. The GCCV-CNN algorithm achieves a high accuracy of 98.06%, which is faster and more robust.

Key words: chest X-ray (CXR) images, CT scan images, COVID-19, Grad-CAM, algorithm combining Grad-CAM color visualization and convolutional neural network (GCCV-CNN)

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