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New machine learning method for image-based diagnosis of COVID-19.
- Source :
-
PLoS ONE . 6/26/2020, Vol. 15 Issue 6, p1-18. 18p. - Publication Year :
- 2020
-
Abstract
- COVID-19 is a worldwide epidemic, as announced by the World Health Organization (WHO) in March 2020. Machine learning (ML) methods can play vital roles in identifying COVID-19 patients by visually analyzing their chest x-ray images. In this paper, a new ML-method proposed to classify the chest x-ray images into two classes, COVID-19 patient or non-COVID-19 person. The features extracted from the chest x-ray images using new Fractional Multichannel Exponent Moments (FrMEMs). A parallel multi-core computational framework utilized to accelerate the computational process. Then, a modified Manta-Ray Foraging Optimization based on differential evolution used to select the most significant features. The proposed method evaluated using two COVID-19 x-ray datasets. The proposed method achieved accuracy rates of 96.09% and 98.09% for the first and second datasets, respectively. [ABSTRACT FROM AUTHOR]
- Subjects :
- *COVID-19
*MACHINE learning
*X-ray imaging
*DIAGNOSIS methods
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 15
- Issue :
- 6
- Database :
- Academic Search Index
- Journal :
- PLoS ONE
- Publication Type :
- Academic Journal
- Accession number :
- 144260105
- Full Text :
- https://doi.org/10.1371/journal.pone.0235187