PMID- 36712413
OWN - NLM
STAT- Publisher
LR  - 20240216
IS  - 1386-7857 (Print)
IS  - 1573-7543 (Electronic)
IS  - 1386-7857 (Linking)
DP  - 2023 Jan 24
TI  - COVID-19 CT-images diagnosis and severity assessment using machine learning 
      algorithm.
PG  - 1-16
LID - 10.1007/s10586-023-03972-5 [doi]
AB  - As a pandemic, the primary evaluation tool for coronavirus (COVID-19) still has 
      serious flaws. To improve the existing situation, all facilities and tools 
      available in this field should be used to combat the pandemic. Reverse 
      transcription polymerase chain reaction is used to evaluate whether or not a 
      person has this virus, but it cannot establish the severity of the illness. In 
      this paper, we propose a simple, reliable, and automatic system to diagnose the 
      severity of COVID-19 from the CT scans into three stages: mild, moderate, and 
      severe, based on the simple segmentation method and three types of features 
      extracted from the CT images, which are ratio of infection, statistical texture 
      features (mean, standard deviation, skewness, and kurtosis), GLCM and GLRLM 
      texture features. Four machine learning techniques (decision trees (DT), 
      K-nearest neighbors (KNN), support vector machines (SVM), and Naive Bayes) are 
      used to classify scans. 1801 scans are divided into four stages based on the CT 
      findings in the scans and the description file found with the datasets. Our 
      proposed model divides into four steps: preprocessing, feature extraction, 
      classification, and performance evaluation. Four machine learning algorithms are 
      used in the classification step: SVM, KNN, DT, and Naive Bayes. By SVM method, 
      the proposed model achieves 99.12%, 98.24%, 98.73%, and 99.9% accuracy for 
      COVID-19 infection segmentation at the normal, mild, moderate, and severe stages, 
      respectively. The area under the curve of the model is 0.99. Finally, our 
      proposed model achieves better performance than state-of-art models. This will 
      help the doctors know the stage of the infection and thus shorten the time and 
      give the appropriate dose of treatment for this stage.
CI  - (c) The Author(s), under exclusive licence to Springer Science+Business Media, LLC, 
      part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or 
      other partner) holds exclusive rights to this article under a publishing 
      agreement with the author(s) or other rightsholder(s); author self-archiving of 
      the accepted manuscript version of this article is solely governed by the terms 
      of such publishing agreement and applicable law.
FAU - Albataineh, Zaid
AU  - Albataineh Z
AD  - Department of Electronic Engineering, Yarmouk University, Irbid, 21163 Jordan. 
      GRID: grid.14440.35. ISNI: 0000 0004 0622 5497
FAU - Aldrweesh, Fatima
AU  - Aldrweesh F
AD  - Department of Computer Engineering, Yarmouk University, Irbid, 21163 Jordan. 
      GRID: grid.14440.35. ISNI: 0000 0004 0622 5497
FAU - Alzubaidi, Mohammad A
AU  - Alzubaidi MA
AD  - Department of Computer Engineering, Yarmouk University, Irbid, 21163 Jordan. 
      GRID: grid.14440.35. ISNI: 0000 0004 0622 5497
LA  - eng
PT  - Journal Article
DEP - 20230124
PL  - Netherlands
TA  - Cluster Comput
JT  - Cluster computing
JID - 101580721
PMC - PMC9871425
OTO - NOTNLM
OT  - COVID-19
OT  - CT scans
OT  - Decision tree
OT  - KNN
OT  - Mild stage
OT  - Moderate stage
OT  - Naive Bayes
OT  - SVM
OT  - Segmentation
OT  - Severe stage
OT  - The severity of infection
COIS- Conflict of interestThe authors declare no conflict of interest.
EDAT- 2023/01/31 06:00
MHDA- 2023/01/31 06:00
PMCR- 2023/01/24
CRDT- 2023/01/30 04:01
PHST- 2022/10/10 00:00 [received]
PHST- 2022/11/20 00:00 [revised]
PHST- 2022/11/26 00:00 [accepted]
PHST- 2023/01/30 04:01 [entrez]
PHST- 2023/01/31 06:00 [pubmed]
PHST- 2023/01/31 06:00 [medline]
PHST- 2023/01/24 00:00 [pmc-release]
AID - 3972 [pii]
AID - 10.1007/s10586-023-03972-5 [doi]
PST - aheadofprint
SO  - Cluster Comput. 2023 Jan 24:1-16. doi: 10.1007/s10586-023-03972-5.