COVID‐19 disease severity assessment using CNN model

• Published in: IET image processing Vol. 15; no. 8; pp. 1814 - 1824
• Main Author: Irmak, Emrah
• Format: Journal Article
• Language: English
• Published: England John Wiley and Sons Inc 01.06.2021; Wiley
• Subjects: Biology and medical computing; Computer vision and image processing techniques; Optical, image and video signal processing; Original Research Paper; Original Research Papers; Patient diagnostic methods and instrumentation; X‐ray techniques: radiography and computed tomography (biomedical imaging/measurement); X‐rays and particle beams (medical uses)
• ISSN: 1751-9659; 1751-9667
• DOI: 10.1049/ipr2.12153

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.