Alaiad Ahmad Imwafak, Mugdadi Esraa Ahmad, Hmeidi Ismail Ibrahim, Obeidat Naser, Abualigah Laith
Computer Information System, Jordan University of Science and Technology, Irbid, Jordan.
Department of Diagnostic Radiology and Nuclear Medicine, Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan.
J Med Biol Eng. 2023;43(2):135-146. doi: 10.1007/s40846-023-00783-2. Epub 2023 Mar 13.
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.
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.
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%.
The proposed model assisted in diagnosing and treating COVID-19 patients and helped improve patient outcomes.
2019年冠状病毒病（COVID-19）在全球范围内产生了重大的社会、医学和经济影响。本研究旨在开发一种深度学习模型，该模型能够根据患者肺部的CT图像预测COVID-19的严重程度。
COVID-19会引发肺部感染，定量逆转录聚合酶链反应（qRT-PCR）是用于检测病毒感染的重要工具。然而，qRT-PCR在检测疾病的严重程度及其对肺部的影响程度方面存在不足。在本文中，我们旨在通过研究被诊断患有该病毒的人的肺部CT扫描来确定COVID-19的严重程度级别。
我们使用了约旦阿卜杜拉国王大学医院的图像；我们从875例病例中收集了包含2205张CT图像的数据集。一名放射科医生将这些图像分为四个严重程度级别：正常、轻度、中度和重度。我们使用了各种深度学习算法来预测肺部疾病的严重程度。结果表明，所使用的最佳深度学习算法是Resnet101，准确率为99.5%，数据损失率为0.03%。
所提出的模型有助于诊断和治疗COVID-19患者，并有助于改善患者的治疗效果。
