COVID-19 anomaly detection and classification method based on supervised machine learning of chest X-ray images

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F21%3A10249300" target="_blank" >RIV/61989100:27240/21:10249300 - isvavai.cz</a>

Result on the web

<a href="https://www.scopus.com/record/display.uri?eid=2-s2.0-85120483972&origin=resultslist&sort=plf-f&src=s&st1=Nedoma%2cJ.&sid=f446027e94f07f944af015edb4136c3a&sot=b&sdt=b&sl=23&s=AUTHOR-NAME%28Nedoma%2c+J.%29&relpos=6&citeCnt=1&searchTerm=&featureToggles=FEATURE_NEW_DOC_DETAILS_EXPORT:1" target="_blank" >https://www.scopus.com/record/display.uri?eid=2-s2.0-85120483972&origin=resultslist&sort=plf-f&src=s&st1=Nedoma%2cJ.&sid=f446027e94f07f944af015edb4136c3a&sot=b&sdt=b&sl=23&s=AUTHOR-NAME%28Nedoma%2c+J.%29&relpos=6&citeCnt=1&searchTerm=&featureToggles=FEATURE_NEW_DOC_DETAILS_EXPORT:1</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.rinp.2021.105045" target="_blank" >10.1016/j.rinp.2021.105045</a>

Result language

angličtina

Original language name

COVID-19 anomaly detection and classification method based on supervised machine learning of chest X-ray images

Original language description

The term COVID-19 is an abbreviation of Coronavirus 2019, which is considered a global pandemic that threatens the lives of millions of people. Early detection of the disease offers ample opportunity of recovery and prevention of spreading. This paper proposes a method for classification and early detection of COVID-19 through image processing using X-ray images. A set of procedures are applied, including preprocessing (image noise removal, image thresholding, and morphological operation), Region of Interest (ROI) detection and segmentation, feature extraction, (Local binary pattern (LBP), Histogram of Gradient (HOG), and Haralick texture features) and classification (K-Nearest Neighbor (KNN) and Support Vector Machine (SVM)). The combinations of the feature extraction operators and classifiers results in six models, namely LBP-KNN, HOG-KNN, Haralick-KNN, LBP-SVM, HOG-SVM, and Haralick-SVM. The six models are tested based on test samples of 5,000 images with the percentage of training of 5-folds cross-validation. The evaluation results show high diagnosis accuracy from 89.2% up to 98.66%. The LBP-KNN model outperforms the other models in which it achieves an average accuracy of 98.66%, a sensitivity of 97.76%, specificity of 100%, and precision of 100%. The proposed method for early detection and classification of COVID-19 through image processing using X-ray images is proven to be usable in which it provides an end-to-end structure without the need for manual feature extraction and manual selection methods.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20200 - Electrical engineering, Electronic engineering, Information engineering

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Results in Physics

ISSN

2211-3797

e-ISSN

—

Volume of the periodical

31

Issue of the periodical within the volume

105045

Country of publishing house

US - UNITED STATES

Number of pages

8

Pages from-to

1-8

UT code for WoS article

000751740300039

EID of the result in the Scopus database

2-s2.0-85120483972