Two-step machine learning to diagnose and predict involvement of lungs in COVID-19 and pneumonia using CT radiomics

Pegah Moradi Khaniabadi*, Yassine Bouchareb*, Humoud Al-Dhuhli, Isaac Shiri, Faiza Al-Kindi, Bita Moradi Khaniabadi, Habib Zaidi, Arman Rahmim

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


Objective: To develop a two-step machine learning (ML) based model to diagnose and predict involvement of
lungs in COVID-19 and non COVID-19 pneumonia patients using CT chest radiomic features.
Methods: Three hundred CT scans (3-classes: 100 COVID-19, 100 pneumonia, and 100 healthy subjects) were
enrolled in this study. Diagnostic task included 3-class classification. Severity prediction score for COVID-19 and
pneumonia was considered as mild (0-25%), moderate (26-50%), and severe (>50%). Whole lungs were
segmented utilizing deep learning-based segmentation. Altogether, 107 features including shape, first-order
histogram, second and high order texture features were extracted. Pearson correlation coefficient (PCC≥90%)
followed by different features selection algorithms were employed. ML-based supervised algorithms (Naïve Bays,
Support Vector Machine, Bagging, Random Forest, K-nearest neighbors, Decision Tree and Ensemble Meta
voting) were utilized. The optimal model was selected based on precision, recall and area-under-curve (AUC) by
randomizing the training/validation, followed by testing using the test set.
Results: Nine pertinent features (2 shape, 1 first-order, and 6 second-order) were obtained after features selection
for both phases. In diagnostic task, the performance of 3-class classification using Random Forest was 0.909
±0.026, 0.907±0.056, 0.902±0.044, 0.939±0.031, and 0.982±0.010 for precision, recall, F1-score, accuracy,
and AUC, respectively. The severity prediction task using Random Forest achieved 0.868±0.123 precision, 0.865
±0.121 recall, 0.853±0.139 F1-score, 0.934±0.024 accuracy, and 0.969±0.022 AUC.
Conclusion: The two-phase ML-based model accurately classified COVID-19 and pneumonia patients using CT
radiomics, and adequately predicted severity of lungs involvement. This 2-steps model showed great potential in
assessing COVID-19 CT images towards improved management of patients.
Original languageEnglish
Article number106165
Number of pages1
JournalComputers in Biology and Medicine
Publication statusPublished - Nov 1 2022
Externally publishedYes


  • COVID-19
  • CT images
  • Diagnosis
  • Machine learning
  • Pneumonia
  • Prediction
  • Radiomics

ASJC Scopus subject areas

  • Health Informatics
  • Computer Science Applications

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