A review of deep learning-based detection methods for COVID-19

被引:85
作者
Subramanian, Nandhini [1 ]
Elharrouss, Omar [1 ]
Al-Maadeed, Somaya [1 ]
Chowdhury, Muhammed [1 ]
机构
[1] Qatar Univ, Coll Engn Comp Sci & Engn, Doha, Qatar
关键词
COVID-19; detection; DL-Based COVID-19 detection; Lung image classification; Coronavirus pandemic; Medical image processing; IMAGES; CT;
D O I
10.1016/j.compbiomed.2022.105233
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
COVID-19 is a fast-spreading pandemic, and early detection is crucial for stopping the spread of infection. Lung images are used in the detection of coronavirus infection. Chest X-ray (CXR) and computed tomography (CT) images are available for the detection of COVID-19. Deep learning methods have been proven efficient and better performing in many computer vision and medical imaging applications. In the rise of the COVID pandemic, researchers are using deep learning methods to detect coronavirus infection in lung images. In this paper, the currently available deep learning methods that are used to detect coronavirus infection in lung images are surveyed. The available methodologies, public datasets, datasets that are used by each method and evaluation metrics are summarized in this paper to help future researchers. The evaluation metrics that are used by the methods are comprehensively compared.
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页数:12
相关论文
共 106 条
  • [1] Abbas A., 2020, ARXIV PREPRINT ARXIV, P13815
  • [2] Adrian, 2020, MED IM
  • [3] COVID-CAPS: A capsule network-based framework for identification of COVID-19 cases from X-ray images
    Afshar, Parnian
    Heidarian, Shahin
    Naderkhani, Farnoosh
    Oikonomou, Anastasia
    Plataniotis, Konstantinos N.
    Mohammadi, Arash
    [J]. PATTERN RECOGNITION LETTERS, 2020, 138 : 638 - 643
  • [4] Explaining machine learning based diagnosis of COVID-19 from routine blood tests with decision trees and criteria graphs
    Alves, Marcos Antonio
    Castro, Giulia Zanon
    Oliveira, Bruno Alberto Soares
    Ferreira, Leonardo Augusto
    Ramirez, Jaime Arturod
    Silva, Rodrigo
    Guimaraes, Frederico Gadelha
    [J]. COMPUTERS IN BIOLOGY AND MEDICINE, 2021, 132
  • [5] [Anonymous], 2020, NIH CHESTXRAYS NIH C
  • [6] [Anonymous], 2020, ECONOMIST
  • [7] [Anonymous], SQUEEZENET ALEXNET L
  • [8] Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks
    Apostolopoulos, Ioannis D.
    Mpesiana, Tzani A.
    [J]. PHYSICAL AND ENGINEERING SCIENCES IN MEDICINE, 2020, 43 (02) : 635 - 640
  • [9] Extracting Possibly Representative COVID-19 Biomarkers from X-ray Images with Deep Learning Approach and Image Data Related to Pulmonary Diseases
    Apostolopoulos, Ioannis D.
    Aznaouridis, Sokratis I.
    Tzani, Mpesiana A.
    [J]. JOURNAL OF MEDICAL AND BIOLOGICAL ENGINEERING, 2020, 40 (03) : 462 - 469
  • [10] A survey on deep learning based approaches for action and gesture recognition in image sequences
    Asadi-Aghbolaghi, Maryam
    Clapes, Albert
    Bellantonio, Marco
    Escalante, Hugo Jair
    Ponce-Lopez, Victor
    Baro, Xavier
    Guyon, Isabelle
    Kasaei, Shohreh
    Escalera, Sergio
    [J]. 2017 12TH IEEE INTERNATIONAL CONFERENCE ON AUTOMATIC FACE AND GESTURE RECOGNITION (FG 2017), 2017, : 476 - 483