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

Thanakorn PhumkueaThakerng WongsirichotKasikrit DamkliangAsma Navasakulpong
Published in: JMIR formative research (2023)
The study found that the MLHC-COVID-19 model effectively differentiated CXR images of COVID-19 patients from those of healthy and non-COVID-19 individuals. It outperformed other state-of-the-art deep learning techniques and showed promising results. These results suggest that the MLHC-COVID-19 model could have been instrumental in early detection and diagnosis of COVID-19 patients, thus playing a significant role in controlling and managing the pandemic. Although the pandemic has slowed down, this model can be adapted and utilized for future similar situations. The model was also integrated into a publicly accessible web-based computer-aided diagnosis system.
Keyphrases
  • sars cov
  • coronavirus disease
  • deep learning
  • machine learning
  • respiratory syndrome coronavirus
  • artificial intelligence
  • high resolution
  • mass spectrometry
  • contrast enhanced
  • clinical evaluation