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COVID‐19 disease severity assessment using CNN model
- Source :
- IET Image Processing, Vol 15, Iss 8, Pp 1814-1824 (2021), Iet Image Processing
- Publication Year :
- 2021
- Publisher :
- Wiley, 2021.
-
Abstract
- Due to the highly infectious nature of the novel coronavirus (COVID‐19) disease, excessive number of patients waits in the line for chest X‐ray examination, which overloads the clinicians and radiologists and negatively affects the patient's treatment, prognosis and control of the pandemic. Now that the clinical facilities such as the intensive care units and the mechanical ventilators are very limited in the face of this highly contagious disease, it becomes quite important to classify the patients according to their severity levels. This paper presents a novel implementation of convolutional neural network (CNN) approach for COVID‐19 disease severity classification (assessment). An automated CNN model is designed and proposed to divide COVID‐19 patients into four severity classes as mild, moderate, severe, and critical with an average accuracy of 95.52% using chest X‐ray images as input. Experimental results on a sufficiently large number of chest X‐ray images demonstrate the effectiveness of CNN model produced with the proposed framework. To the best of the author's knowledge, this is the first COVID‐19 disease severity assessment study with four stages (mild vs. moderate vs. severe vs. critical) using a sufficiently large number of X‐ray images dataset and CNN whose almost all hyper‐parameters are automatically tuned by the grid search optimiser.
- Subjects :
- 2019-20 coronavirus outbreak
Coronavirus disease 2019 (COVID-19)
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
02 engineering and technology
Disease
Convolutional neural network
QA76.75-76.765
Mechanical ventilator
Disease severity
Intensive care
0202 electrical engineering, electronic engineering, information engineering
Photography
Medicine
Computer software
Electrical and Electronic Engineering
TR1-1050
business.industry
020206 networking & telecommunications
Pattern recognition
Original Research Paper
Signal Processing
020201 artificial intelligence & image processing
Computer Vision and Pattern Recognition
Artificial intelligence
business
Original Research Papers
Software
Subjects
Details
- Language :
- English
- ISSN :
- 17519659 and 17519667
- Volume :
- 15
- Issue :
- 8
- Database :
- OpenAIRE
- Journal :
- IET Image Processing
- Accession number :
- edsair.doi.dedup.....a46a285aa430dd5da347c36eaa62d7d6