PMID- 34764548
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20220716
IS  - 1573-7497 (Electronic)
IS  - 0924-669X (Print)
IS  - 0924-669X (Linking)
VI  - 51
IP  - 2
DP  - 2021
TI  - Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional 
      neural network.
PG  - 854-864
LID - 10.1007/s10489-020-01829-7 [doi]
AB  - Chest X-ray is the first imaging technique that plays an important role in the 
      diagnosis of COVID-19 disease. Due to the high availability of large-scale 
      annotated image datasets, great success has been achieved using convolutional 
      neural networks (CNN s) for image recognition and classification. However, due to 
      the limited availability of annotated medical images, the classification of 
      medical images remains the biggest challenge in medical diagnosis. Thanks to 
      transfer learning, an effective mechanism that can provide a promising solution 
      by transferring knowledge from generic object recognition tasks to 
      domain-specific tasks. In this paper, we validate and a deep CNN, called 
      Decompose, Transfer, and Compose (DeTraC), for the classification of COVID-19 
      chest X-ray images. DeTraC can deal with any irregularities in the image dataset 
      by investigating its class boundaries using a class decomposition mechanism. The 
      experimental results showed the capability of DeTraC in the detection of COVID-19 
      cases from a comprehensive image dataset collected from several hospitals around 
      the world. High accuracy of 93.1% (with a sensitivity of 100%) was achieved by 
      DeTraC in the detection of COVID-19 X-ray images from normal, and severe acute 
      respiratory syndrome cases.
CI  - (c) The Author(s) 2020.
FAU - Abbas, Asmaa
AU  - Abbas A
AD  - Mathematics Department, Faculty of Science, Assiut University, Assiut, Egypt. 
      GRID: grid.252487.e. ISNI: 0000 0000 8632 679X
FAU - Abdelsamea, Mohammed M
AU  - Abdelsamea MM
AD  - Mathematics Department, Faculty of Science, Assiut University, Assiut, Egypt. 
      GRID: grid.252487.e. ISNI: 0000 0000 8632 679X
AD  - School of Computing and Digital Technology, Birmingham City University, 
      Birmingham, UK. GRID: grid.19822.30. ISNI: 0000 0001 2180 2449
FAU - Gaber, Mohamed Medhat
AU  - Gaber MM
AD  - School of Computing and Digital Technology, Birmingham City University, 
      Birmingham, UK. GRID: grid.19822.30. ISNI: 0000 0001 2180 2449
LA  - eng
PT  - Journal Article
DEP - 20200905
PL  - Netherlands
TA  - Appl Intell (Dordr)
JT  - Applied intelligence (Dordrecht, Netherlands)
JID - 9918284258306676
PMC - PMC7474514
OTO - NOTNLM
OT  - COVID-19 detection
OT  - Chest X-ray images
OT  - Covolutional neural networks
OT  - Data irregularities
OT  - DeTraC
EDAT- 2020/09/05 00:00
MHDA- 2020/09/05 00:01
PMCR- 2020/09/05
CRDT- 2021/11/12 06:57
PHST- 2020/09/05 00:00 [pubmed]
PHST- 2020/09/05 00:01 [medline]
PHST- 2021/11/12 06:57 [entrez]
PHST- 2020/09/05 00:00 [pmc-release]
AID - 1829 [pii]
AID - 10.1007/s10489-020-01829-7 [doi]
PST - ppublish
SO  - Appl Intell (Dordr). 2021;51(2):854-864. doi: 10.1007/s10489-020-01829-7. Epub 
      2020 Sep 5.