Machine-learning classification of texture features of portable chest X-ray accurately classifies COVID-19 lung infection

Lal Hussain, Tony Nguyen, Haifang Li, Adeel A. Abbasi, Kashif J. Lone, Zirun Zhao, Mahnoor Zaib, Anne Chen, Tim Q. Duong

Research output: Contribution to journalArticlepeer-review

80 Scopus citations


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 and methods: 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.

Original languageEnglish (US)
Article number88
JournalBioMedical Engineering Online
Issue number1
StatePublished - Dec 2020
Externally publishedYes


  • COVID-19
  • Classification
  • Feature extraction
  • Machine learning
  • Morphological
  • Texture

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Biomaterials
  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging


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