tetano
Editor, Senior Moderator
J Med Biol Eng
. 2023;43(2):135-146.
doi: 10.1007/s40846-023-00783-2. Epub 2023 Mar 13.
Predicting the Severity of COVID-19 from Lung CT Images Using Novel Deep Learning
Ahmad Imwafak Alaiad[SUP] 1 [/SUP], Esraa Ahmad Mugdadi[SUP] 1 [/SUP], Ismail Ibrahim Hmeidi[SUP] 1 [/SUP], Naser Obeidat[SUP] 2 [/SUP], Laith Abualigah[SUP] 3 4 5 6 7 8 [/SUP]
Affiliations
Abstract
Purpose: Coronavirus 2019 (COVID-19) had major social, medical, and economic impacts globally. The study aims to develop a deep-learning model that can predict the severity of COVID-19 in patients based on CT images of their lungs.
Methods: COVID-19 causes lung infections, and qRT-PCR is an essential tool used to detect virus infection. However, qRT-PCR is inadequate for detecting the severity of the disease and the extent to which it affects the lung. In this paper, we aim to determine the severity level of COVID-19 by studying lung CT scans of people diagnosed with the virus.
Results: We used images from King Abdullah University Hospital in Jordan; we collected our dataset from 875 cases with 2205 CT images. A radiologist classified the images into four levels of severity: normal, mild, moderate, and severe. We used various deep-learning algorithms to predict the severity of lung diseases. The results show that the best deep-learning algorithm used is Resnet101, with an accuracy score of 99.5% and a data loss rate of 0.03%.
Conclusion: The proposed model assisted in diagnosing and treating COVID-19 patients and helped improve patient outcomes.
Keywords: COVID-19; Deep learning; Lungs ct; Mild; Moderate; Normal; Severe; Severity.
. 2023;43(2):135-146.
doi: 10.1007/s40846-023-00783-2. Epub 2023 Mar 13.
Predicting the Severity of COVID-19 from Lung CT Images Using Novel Deep Learning
Ahmad Imwafak Alaiad[SUP] 1 [/SUP], Esraa Ahmad Mugdadi[SUP] 1 [/SUP], Ismail Ibrahim Hmeidi[SUP] 1 [/SUP], Naser Obeidat[SUP] 2 [/SUP], Laith Abualigah[SUP] 3 4 5 6 7 8 [/SUP]
Affiliations
- PMID: 37077696
- PMCID: PMC10010231
- DOI: 10.1007/s40846-023-00783-2
Abstract
Purpose: Coronavirus 2019 (COVID-19) had major social, medical, and economic impacts globally. The study aims to develop a deep-learning model that can predict the severity of COVID-19 in patients based on CT images of their lungs.
Methods: COVID-19 causes lung infections, and qRT-PCR is an essential tool used to detect virus infection. However, qRT-PCR is inadequate for detecting the severity of the disease and the extent to which it affects the lung. In this paper, we aim to determine the severity level of COVID-19 by studying lung CT scans of people diagnosed with the virus.
Results: We used images from King Abdullah University Hospital in Jordan; we collected our dataset from 875 cases with 2205 CT images. A radiologist classified the images into four levels of severity: normal, mild, moderate, and severe. We used various deep-learning algorithms to predict the severity of lung diseases. The results show that the best deep-learning algorithm used is Resnet101, with an accuracy score of 99.5% and a data loss rate of 0.03%.
Conclusion: The proposed model assisted in diagnosing and treating COVID-19 patients and helped improve patient outcomes.
Keywords: COVID-19; Deep learning; Lungs ct; Mild; Moderate; Normal; Severe; Severity.