Machine-learning classification of texture features of portable chest X-ray accurately classifies COVID-19 lung infection.
Biomed Eng Online
; 19(1): 88, 2020 Nov 25.
Article
in English
| MEDLINE | ID: covidwho-945214
ABSTRACT
BACKGROUND:
The large volume and suboptimal image quality of portable chest X-rays (CXRs) as a result of the COVID-19 pandemic could post significant challenges for radiologists and frontline physicians. Deep-learning artificial intelligent (AI) methods have the potential to help improve diagnostic efficiency and accuracy for reading portable CXRs.PURPOSE:
The study aimed at developing an AI imaging analysis tool to classify COVID-19 lung infection based on portable CXRs. MATERIALS ANDMETHODS:
Public datasets of COVID-19 (N = 130), bacterial pneumonia (N = 145), non-COVID-19 viral pneumonia (N = 145), and normal (N = 138) CXRs were analyzed. Texture and morphological features were extracted. Five supervised machine-learning AI algorithms were used to classify COVID-19 from other conditions. Two-class and multi-class classification were performed. Statistical analysis was done using unpaired two-tailed t tests with unequal variance between groups. Performance of classification models used the receiver-operating characteristic (ROC) curve analysis.RESULTS:
For the two-class classification, the accuracy, sensitivity and specificity were, respectively, 100%, 100%, and 100% for COVID-19 vs normal; 96.34%, 95.35% and 97.44% for COVID-19 vs bacterial pneumonia; and 97.56%, 97.44% and 97.67% for COVID-19 vs non-COVID-19 viral pneumonia. For the multi-class classification, the combined accuracy and AUC were 79.52% and 0.87, respectively.CONCLUSION:
AI classification of texture and morphological features of portable CXRs accurately distinguishes COVID-19 lung infection in patients in multi-class datasets. Deep-learning methods have the potential to improve diagnostic efficiency and accuracy for portable CXRs.Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Image Processing, Computer-Assisted
/
Radiography, Thoracic
/
Tomography, X-Ray Computed
/
Machine Learning
/
COVID-19
/
Lung Diseases
Type of study:
Diagnostic study
/
Experimental Studies
/
Randomized controlled trials
Topics:
Long Covid
Limits:
Humans
Language:
English
Journal:
Biomed Eng Online
Journal subject:
Biomedical Engineering
Year:
2020
Document Type:
Article
Affiliation country:
S12938-020-00831-x
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