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CCBlock: an effective use of deep learning for automatic diagnosis of COVID-19 using X-ray images

CCBlock: an effective use of deep learning for automatic diagnosis of COVID-19 using X-ray images ProposeTroubling countries one after another, the COVID-19 pandemic has dramatically affected the health and well-being of the world’s population. The disease may continue to persist more extensively due to the increasing number of new cases daily, the rapid spread of the virus, and delay in the PCR analysis results. Therefore, it is necessary to consider developing assistive methods for detecting and diagnosing the COVID-19 to eradicate the spread of the novel coronavirus among people. Based on convolutional neural networks (CNNs), automated detection systems have shown promising results of diagnosing patients with the COVID-19 through radiography; thus, they are introduced as a workable solution to the COVID-19 diagnosis.Materials and methodsBased on the enhancement of the classical visual geometry group (VGG) network with the convolutional COVID block (CCBlock), an efficient screening model was proposed in this study to diagnose and distinguish patients with the COVID-19 from those with pneumonia and the healthy people through radiography. The model testing dataset included 1828 X-ray images available on public platforms. Three hundred and ten images were showing confirmed COVID-19 cases, 864 images indicating pneumonia cases, and 654 images showing healthy people.ResultsAccording to the test results, enhancing the classical VGG network with radiography provided the highest diagnosis performance and overall accuracy of 98.52% for two classes as well as accuracy of 95.34% for three classes.ConclusionsAccording to the results, using the enhanced VGG deep neural network can help radiologists automatically diagnose the COVID-19 through radiography. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Research on Biomedical Engineering Springer Journals

CCBlock: an effective use of deep learning for automatic diagnosis of COVID-19 using X-ray images

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Publisher
Springer Journals
Copyright
Copyright © Sociedade Brasileira de Engenharia Biomedica 2020
ISSN
2446-4732
eISSN
2446-4740
DOI
10.1007/s42600-020-00110-7
Publisher site
See Article on Publisher Site

Abstract

ProposeTroubling countries one after another, the COVID-19 pandemic has dramatically affected the health and well-being of the world’s population. The disease may continue to persist more extensively due to the increasing number of new cases daily, the rapid spread of the virus, and delay in the PCR analysis results. Therefore, it is necessary to consider developing assistive methods for detecting and diagnosing the COVID-19 to eradicate the spread of the novel coronavirus among people. Based on convolutional neural networks (CNNs), automated detection systems have shown promising results of diagnosing patients with the COVID-19 through radiography; thus, they are introduced as a workable solution to the COVID-19 diagnosis.Materials and methodsBased on the enhancement of the classical visual geometry group (VGG) network with the convolutional COVID block (CCBlock), an efficient screening model was proposed in this study to diagnose and distinguish patients with the COVID-19 from those with pneumonia and the healthy people through radiography. The model testing dataset included 1828 X-ray images available on public platforms. Three hundred and ten images were showing confirmed COVID-19 cases, 864 images indicating pneumonia cases, and 654 images showing healthy people.ResultsAccording to the test results, enhancing the classical VGG network with radiography provided the highest diagnosis performance and overall accuracy of 98.52% for two classes as well as accuracy of 95.34% for three classes.ConclusionsAccording to the results, using the enhanced VGG deep neural network can help radiologists automatically diagnose the COVID-19 through radiography.

Journal

Research on Biomedical Engineering – Springer Journals

Published: Mar 1, 2022

Keywords: COVID-19; X-ray radiographs; Transfer learning; Deep learning; Automated detection

References

  • Correlation of chest CT and RT-PCR testing in coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases
    Ai, T; Yang, Z; Hou, H
  • New SARS-like virus in China triggers alarm
    Cohen, J; Normile, D
  • Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR
    Corman, VM; Landt, O; Kaiser, M
  • Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China
    Huang, C; Wang, Y; Li, X; Ren, L; Zhao, J; Hu, Y; Zhang, L; Fan, G; Xu, J; Gu, X; Cheng, Z; Yu, T; Xia, J; Wei, Y; Wu, W; Xie, X; Yin, W; Li, H; Liu, M; Xiao, Y; Gao, H; Guo, L; Xie, J; Wang, G; Jiang, R; Gao, Z; Jin, Q; Wang, J; Cao, B
  • Contour-aware multi-label chest X-ray organ segmentation
    Kholiavchenko, M; Sirazitdinov, I; Kubrak, K
  • Early transmission dynamics in Wuhan, China, of novel coronavirus–infected pneumonia
    Li, Q; Guan, X; Wu, P; Wang, X; Zhou, L; Tong, Y; Ren, R; Leung, KSM; Lau, EHY; Wong, JY; Xing, X; Xiang, N; Wu, Y; Li, C; Chen, Q; Li, D; Liu, T; Zhao, J; Liu, M; Tu, W; Chen, C; Jin, L; Yang, R; Wang, Q; Zhou, S; Wang, R; Liu, H; Luo, Y; Liu, Y; Shao, G; Li, H; Tao, Z; Yang, Y; Deng, Z; Liu, B; Ma, Z; Zhang, Y; Shi, G; Lam, TTY; Wu, JT; Gao, GF; Cowling, BJ; Yang, B; Leung, GM; Feng, Z
  • Coronavirus: covid-19 has killed more people than SARS and MERS combined, despite lower case fatality rate
    Mahase, E
  • Coronavirus infections—more than just the common cold
    Paules, C; Marston, H; Fauci, A
  • ImageNet large scale visual recognition challenge
    Russakovsky, O; Deng, J; Su, H; Krause, J; Satheesh, S; Ma, S; Huang, Z; Karpathy, A; Khosla, A; Bernstein, M; Berg, AC; Fei-Fei, L
  • World Health Organization declares global emergency: a review of the 2019 novel coronavirus (COVID-19)
    Sohrabi, C; Alsafi, Z; O'Neill, N; Khan, M; Kerwan, A; al-Jabir, A; Iosifidis, C; Agha, R
  • Detection of SARS-CoV-2 in different types of clinical specimens
    Wang, W; Xu, Y; Gao, R; Lu, R; Han, K; Wu, G; Tan, W
  • A novel coronavirus from patients with pneumonia in China, 2019
    Zhu, N; Zhang, D; Wang, W