A survey of machine learning techniques for detecting and diagnosing COVID-19 from imaging

被引:2
作者
Panday, Aishwarza [1 ]
Kabir, Muhammad Ashad [2 ]
Chowdhury, Nihad Karim [3 ]
机构
[1] Stamford Univ, Dept Comp Sci & Engn, Dhaka 1217, Bangladesh
[2] Charles Sturt Univ, Sch Comp & Math, Bathurst, NSW 2795, Australia
[3] Univ Chittagong, Dept Comp Sci & Engn, Chittagong 4349, Bangladesh
关键词
COVID-19; machine learning; deep learning; detection; classification; diagnosing; X-ray; CT scan; CORONAVIRUS DISEASE COVID-19; X-RAY; CT IMAGES; CLASSIFICATION; AUGMENTATION; METHODOLOGY; SELECTION; FEATURES; NETWORK; GAN;
D O I
10.15302/J-QB-021-0274
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Background: Due to the limited availability and high cost of the reverse transcription-polymerase chain reaction (RT- PCR) test, many studies have proposed machine learning techniques for detecting COVID-19 from medical imaging. The purpose of this study is to systematically review, assess and synthesize research articles that have used different machine learning techniques to detect and diagnose COVID-19 from chest X-ray and CT scan images. Methods: A structured literature search was conducted in the relevant bibliographic databases to ensure that the survey solely centered on reproducible and high-quality research. We selected papers based on our inclusion criteria. Results: In this survey, we reviewed 98 articles that fulfilled our inclusion criteria. We have surveyed a complete pipeline of chest imaging analysis techniques related to COVID-19, including data collection, pre-processing, feature extraction, classification, and visualization. We have considered CT scans and X-rays as both are widely used to describe the latest developments in medical imaging to detect COVID-19. Conclusions: This survey provides researchers with valuable insights into different machine learning techniques and their performance in the detection and diagnosis of COVID-19 from chest imaging. At the end, the challenges and limitations in detecting COVID-19 using machine learning techniques and the future direction of research are discussed.
引用
收藏
页码:188 / 207
页数:20
相关论文
共 119 条
  • [1] Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network
    Abbas, Asmaa
    Abdelsamea, Mohammed M.
    Gaber, Mohamed Medhat
    [J]. APPLIED INTELLIGENCE, 2021, 51 (02) : 854 - 864
  • [2] 4S-DT: Self-Supervised Super Sample Decomposition for Transfer Learning With Application to COVID-19 Detection
    Abbas, Asmaa
    Abdelsamea, Mohammed M.
    Gaber, Mohamed Medhat
    [J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2021, 32 (07) : 2798 - 2808
  • [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] Advance Warning Methodologies for COVID-19 Using Chest X-Ray Images
    Ahishali, Mete
    Degerli, Aysen
    Yamac, Mehmet
    Kiranyaz, Serkan
    Chowdhury, Muhammad E. H.
    Hameed, Khalid
    Hamid, Tahir
    Mazhar, Rashid
    Gabbouj, Moncef
    [J]. IEEE ACCESS, 2021, 9 : 41052 - 41065
  • [5] Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices
    Ahuja, Sakshi
    Panigrahi, Bijaya Ketan
    Dey, Nilanjan
    Rajinikanth, Venkatesan
    Gandhi, Tapan Kumar
    [J]. APPLIED INTELLIGENCE, 2021, 51 (01) : 571 - 585
  • [6] Correlation of Chest CT and RT-PCR Testing for Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases
    Ai, Tao
    Yang, Zhenlu
    Hou, Hongyan
    Zhan, Chenao
    Chen, Chong
    Lv, Wenzhi
    Tao, Qian
    Sun, Ziyong
    Xia, Liming
    [J]. RADIOLOGY, 2020, 296 (02) : E32 - E40
  • [7] Al-antari M. A., 2020, RES SQUARE, DOI [10.21203/rs.3.rs-36353/v1, DOI 10.21203/RS.3.RS-36353/V1]
  • [8] Al-karawi D, 2020, MEDRXIV, DOI DOI 10.1101/2020.04.13.20063479
  • [9] Efficient GAN-based Chest Radiographs (CXR) augmentation to diagnose coronavirus disease pneumonia
    Albahli, Saleh
    [J]. INTERNATIONAL JOURNAL OF MEDICAL SCIENCES, 2020, 17 (10): : 1439 - 1448
  • [10] Role of biological Data Mining and Machine Learning Techniques in Detecting and Diagnosing the Novel Coronavirus (COVID-19): A Systematic Review
    Albahri, A. S.
    Hamid, Rula A.
    Alwan, Jwan K.
    Al-qays, Z. T.
    Zaidan, A. A.
    Zaidan, B. B.
    Albahri, A. O. S.
    AlAmoodi, A. H.
    Khlaf, Jamal Mawlood
    Almahdi, E. M.
    Thabet, Eman
    Hadi, Suha M.
    Mohammed, K., I
    Alsalem, M. A.
    Al-Obaidi, Jameel R.
    Madhloom, H. T.
    [J]. JOURNAL OF MEDICAL SYSTEMS, 2020, 44 (07)