Classification of COVID-19 Infection in Posteroanterior Chest X-rays
2020年4月21日 更新者:Dascena
Classification of COVID-19 Infection in Posteroanterior Chest X-rays With Common Deep Learning Architectures
The objective of this study is to assess three configurations of two convolutional deep neural network architectures for the classification of COVID-19 PCX images.
研究概览
详细说明
The December 2019 outbreak of COVID-19 has now evolved into a public health emergency of global concern.
Given the rapid spread of infection, the rapid depletion of hospital resources due to high influxes of patients, and the current absence of specific therapeutic drugs and vaccines for treatment of COVID-19 infection, it is essential to detect onset of the disease at its early stages.
Radiological examinations, the most common of which are posteroanterior chest X-ray (PCX) images, play an important role in the diagnosis of COVID-19.
The objective of this study is to assess three configurations of two convolutional deep neural network architectures for the classification of COVID-19 PCX images.
The primary experimental dataset consisted of 115 COVID-19 positive and 115 COVID-19 negative PCX images, the latter comprising roughly equally many pneumonia, emphysema, fibrosis, and healthy images (230 total images).
Two common convolutional neural network architectures were used, VGG16 and DenseNet121, the former initially configured with off-the-shelf (OTS) parameters and the latter with either OTS or exclusively X-ray trained (XRT) parameters.
The OTS parameters were derived from training on the ImageNet dataset, while the XRT parameters were obtained from training on the NIH chest X-ray dataset, ChestX-ray14.
A final, densely connected layer was added to each model, the parameters of which were trained and validated on 87% of images from the experimental dataset, for the task of binary classification of images as COVID-19 positive or COVID-19 negative.
Each model was tested on a hold-out set consisting of the other 13% of images.
Performance metrics were calculated as the average over five random 80%-20% splits of the images into training and validation sets, respectively.
研究类型
观察性的
注册 (实际的)
230
联系人和位置
本节提供了进行研究的人员的详细联系信息,以及有关进行该研究的地点的信息。
学习地点
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California
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Oakland、California、美国、94612-2603
- Dascena
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参与标准
研究人员寻找符合特定描述的人,称为资格标准。这些标准的一些例子是一个人的一般健康状况或先前的治疗。
资格标准
适合学习的年龄
18年 及以上 (成人、年长者)
接受健康志愿者
是的
有资格学习的性别
全部
取样方法
非概率样本
研究人群
115 COVID-19 single PCX images and 115 non COVID-19 single PCX images (230 images total), collected from 81 unique COVID-19 patients and 91 unique non COVID-19 patients.
描述
Inclusion Criteria:
- Single PCX images collected from patients over 18 years of age
Exclusion Criteria:
- CT scans composed of multiple concerted X-rays
- Single PCX images collected from patients under 18 years of age
学习计划
本节提供研究计划的详细信息,包括研究的设计方式和研究的衡量标准。
研究是如何设计的?
设计细节
队列和干预
团体/队列 |
干预/治疗 |
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COVID-19 Patients
Single posteroanterior (or "front-on") X-rays collected from COVID-19 patients
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Convolutional neural network for classification of COVID-19 from chest X-rays
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Non COVID-19 Patients
Single posteroanterior (or "front-on") X-rays collected from subsets of non COVID-19 patients
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Convolutional neural network for classification of COVID-19 from chest X-rays
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研究衡量的是什么?
主要结果指标
结果测量 |
措施说明 |
大体时间 |
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Identification of COVID-19
大体时间:Through study completion, an average of 2 months
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Identification of COVID-19 infection from chest X-ray analysis
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Through study completion, an average of 2 months
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合作者和调查者
在这里您可以找到参与这项研究的人员和组织。
赞助
出版物和有用的链接
负责输入研究信息的人员自愿提供这些出版物。这些可能与研究有关。
有用的网址
研究记录日期
这些日期跟踪向 ClinicalTrials.gov 提交研究记录和摘要结果的进度。研究记录和报告的结果由国家医学图书馆 (NLM) 审查,以确保它们在发布到公共网站之前符合特定的质量控制标准。
研究主要日期
学习开始 (实际的)
2020年4月1日
初级完成 (实际的)
2020年4月17日
研究完成 (实际的)
2020年4月17日
研究注册日期
首次提交
2020年4月20日
首先提交符合 QC 标准的
2020年4月21日
首次发布 (实际的)
2020年4月24日
研究记录更新
最后更新发布 (实际的)
2020年4月24日
上次提交的符合 QC 标准的更新
2020年4月21日
最后验证
2020年4月1日
更多信息
此信息直接从 clinicaltrials.gov 网站检索,没有任何更改。如果您有任何更改、删除或更新研究详细信息的请求,请联系 register@clinicaltrials.gov. clinicaltrials.gov 上实施更改,我们的网站上也会自动更新.
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Indonesia University招聘中
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Endourage, LLC招聘中长COVID | 长Covid19 | 后急性 COVID-19 | 长途 COVID | 长途 COVID-19 | 急性 COVID-19 后综合症美国
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