Pre-processing methods in chest X-ray image classification.
PLoS One
; 17(4): e0265949, 2022.
Article
in English
| MEDLINE | ID: covidwho-1775451
ABSTRACT
BACKGROUND:
The SARS-CoV-2 pandemic began in early 2020, paralyzing human life all over the world and threatening our security. Thus, the need for an effective, novel approach to diagnosing, preventing, and treating COVID-19 infections became paramount.METHODS:
This article proposes a machine learning-based method for the classification of chest X-ray images. We also examined some of the pre-processing methods such as thresholding, blurring, and histogram equalization.RESULTS:
We found the F1-score results rose to 97%, 96%, and 99% for the three analyzed classes healthy, COVID-19, and pneumonia, respectively.CONCLUSION:
Our research provides proof that machine learning can be used to support medics in chest X-ray classification and improving pre-processing leads to improvements in accuracy, precision, recall, and F1-scores.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Deep Learning
/
COVID-19
Type of study:
Prognostic study
Limits:
Humans
Language:
English
Journal:
PLoS One
Journal subject:
Science
/
Medicine
Year:
2022
Document Type:
Article
Affiliation country:
Journal.pone.0265949
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