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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20200 - Electrical engineering, Electronic engineering, Information engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název periodika

Results in Physics

ISSN

2211-3797

e-ISSN

—

Svazek periodika

31

Číslo periodika v rámci svazku

105045

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

8

Strana od-do

1-8

Kód UT WoS článku

000751740300039

EID výsledku v databázi Scopus

2-s2.0-85120483972