Comparison of machine learning algorithms for chest X-ray image COVID-19 classification

Abstract Artificial Intelligence and Machine Learning algorithms were used to identify the coronavirus (COVID-19) from X-ray photos of the chest. The authors propose a model for early coronavirus detection based on image filtering strategies and a hybrid feature selection model in this analysis. Tra...

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Bibliographic Details
Published inJournal of physics. Conference series Vol. 1933; no. 1; pp. 12040 - 12045
Main Authors Samsir, Samsir, Sitorus, Jimmi Hendrik P., Zulkifli, Ritonga, Zuriani, Nasution, Fitri Aini, Watrianthos, Ronal
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 01.06.2021
Subjects
Algorithms
Artificial intelligence
Chest
Computed tomography
Coronaviruses
COVID-19
Image classification
Image filters
Machine learning
Medical imaging
Physics
Support vector machines
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Summary:Abstract Artificial Intelligence and Machine Learning algorithms were used to identify the coronavirus (COVID-19) from X-ray photos of the chest. The authors propose a model for early coronavirus detection based on image filtering strategies and a hybrid feature selection model in this analysis. Traditional statistical and machine learning methods are used to derive these attributes from CT images. The Confusion Matrix for infected COVID-19 patients and regular patients was obtained using Support Vector Machine and K-Nearest Neighbor to classify the features chosen. The output of the two approaches can be compared. The various techniques’ performance shows that the Support Vector Machine achieves the highest precision of 97% compared to the K-Nearest Neighbor with a precision of 86%.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1933/1/012040