Predicting the Severity of COVID-19 from Lung CT Images Using Novel Deep Learning.
Predicting the Severity of COVID-19 from Lung CT Images Using Novel Deep Learning.
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DOI：
10.1007/s40846-023-00783-2
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发表时间：
2023
影响因子：
2
通讯作者：
Abualigah, Laith
中科院分区：
文献类型：
--
作者：
Alaiad, Ahmad Imwafak;Mugdadi, Esraa Ahmad;Hmeidi, Ismail Ibrahim;Obeidat, Naser;Abualigah, Laith
2019冠状病毒（COVID-19）在全球范围内产生了重大的社会、医疗和经济影响。该研究旨在开发一种深度学习模型，可以根据肺部CT图像预测患者的COVID-19严重程度。 COVID-19导致肺部感染，qRT-PCR是用于检测病毒感染的重要工具。然而，qRT-PCR不足以检测疾病的严重程度及其对肺部的影响程度。在这篇论文中，我们的目标是通过研究被诊断感染COVID-19的人的肺部CT扫描来确定COVID-19的严重程度。 我们使用约旦阿卜杜拉国王大学医院的图像;我们收集了875例病例的数据集，其中2205张CT图像。放射科医生将图像分为四个严重程度：正常、轻度、中度和重度。我们使用各种深度学习算法来预测肺部疾病的严重程度。结果显示，使用的最佳深度学习算法是Resnet 101，准确率为99.5%，数据丢失率为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预后。
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.
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影响因子：
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作者：
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通讯作者：
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DOI：
10.1007/s11548-020-02286-w
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DOI：
10.3390/diagnostics11050893
发表时间：
2021-05-17
期刊：
Diagnostics (Basel, Switzerland)
影响因子：
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作者：
Qiblawey Y;Tahir A;Chowdhury MEH;Khandakar A;Kiranyaz S;Rahman T;Ibtehaz N;Mahmud S;Maadeed SA;Musharavati F;Ayari MA
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