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This paper is in the following e-collection/theme issue:

Formative Evaluation of Digital Health Interventions (3606) Clinical Information and Decision Making (2164) Decision Support for Health Professionals (1712) Imaging Informatics (356) Machine Learning (2326) Artificial Intelligence (2479) JMIR Theme Issue: COVID-19 Special Issue (2571)

Published on in Vol 7 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/42324, first published .
Scientist in blue gloves handles vial in lab, using centrifuge for sample preparation.

Classifying COVID-19 Patients From Chest X-ray Images Using Hybrid Machine Learning Techniques: Development and Evaluation

Classifying COVID-19 Patients From Chest X-ray Images Using Hybrid Machine Learning Techniques: Development and Evaluation

Authors of this article:

Thanakorn Phumkuea1 Author Orcid Image ;   Thakerng Wongsirichot2 Author Orcid Image ;   Kasikrit Damkliang2 Author Orcid Image ;   Asma Navasakulpong3 Author Orcid Image
Article Authors Cited by (4) Tweetations (3) Metrics

Journals

  1. Irede E, Aworinde O, Lekan O, Amienghemhen O, Okonkwo T, Onivefu A, Ifijen I. Medical imaging: A Critical Review on X-ray Imaging for the Detection of Infection. Biomedical Materials & Devices 2026;4(1):1 View
  2. Yang X, Ma H, Zhu M, Song X, Feng J. Integrated Deep Learning Surveillance of Unknown Pathogens with Pandemic Potential Using Pneumonia of Unknown Etiology. Pathogens 2026;15(4):413 View
  3. Yenurkar G, Mal S, Syamala A, Shambharkar S, Tarone A, Awathare U, Mudriwar P, Shah R, Indurkar B. Leveraging deep residual learning and gradient boosting for COVID-19 detection in chest radiographs. Discover Artificial Intelligence 2026;6(1) View

Books/Policy Documents

  1. Abdelhamid A, Akinniyi O, Saleh G, Khalifa F. Intelligent Systems, Blockchain, and Communication Technologies. View