Coronavirus disease (COVID-19) detection using X-ray images and enhanced DenseNet.
Coronavirus disease (COVID-19) detection using X-ray images and enhanced DenseNet.
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DOI：
10.1016/j.asoc.2021.107645
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发表时间：
2021-10
影响因子：
8.7
通讯作者：
Shiraz M
中科院分区：
文献类型：
--
作者：
Albahli S;Ayub N;Shiraz M
源自中国的2019新型冠状病毒（COVID-19）在其他国家的人群中迅速传播。根据世界卫生组织（WHO）的数据，截至1月底，已有超过1.04亿人受到COVID-19的影响，其中超过200万人死亡。由于常规病例增加，医院可提供的COVID-19检测试剂盒数量减少。因此，应该引入自动检测系统作为快速的替代诊断方法，以防止COVID-19在人类之间传播。为此，在本分析中提出了三种不同的BiT模型：DenseNet，InceptionV 3和Inception-ResNetV 4，用于使用胸部X射线照片诊断冠状病毒肺炎感染的患者。这三个模型使用5折交叉验证给出并检查了受试者操作特征（ROC）分析和不确定性矩阵。我们已经进行了模拟，这些模拟显示，预训练的DenseNet模型具有最好的分类效率，在提出的其他两个模型中达到92%（Inception V3的准确率为83.47%，Inception-ResNetV 4的准确率为85.57%）。
The 2019 novel coronavirus (COVID-19) originating from China, has spread rapidly among people living in other countries. According to the World Health Organization (WHO), by the end of January, more than 104 million people have been affected by COVID-19, including more than 2 million deaths. The number of COVID-19 test kits available in hospitals is reduced due to the increase in regular cases. Therefore, an automatic detection system should be introduced as a fast, alternative diagnostic to prevent COVID-19 from spreading among humans. For this purpose, three different BiT models: DenseNet, InceptionV3, and Inception-ResNetV4 have been proposed in this analysis for the diagnosis of patients infected with coronavirus pneumonia using X-ray radiographs in the chest. These three models give and examine Receiver Operating Characteristic (ROC) analyses and uncertainty matrices, using 5-fold cross-validation. We have performed the simulations which have visualized that the pre-trained DenseNet model has the best classification efficiency with 92% among two other models proposed (83.47% accuracy for inception V3 and 85.57% accuracy for Inception-ResNetV4).
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深度学习利用 CT 图像准确诊断新型冠状病毒 (COVID-19)
DOI：
10.1109/tcbb.2021.3065361
发表时间：
2021-11
期刊：
IEEE/ACM transactions on computational biology and bioinformatics
影响因子：
--
作者：
Song Y;Zheng S;Li L;Zhang X;Zhang X;Huang Z;Chen J;Wang R;Zhao H;Chong Y;Shen J;Zha Y;Yang Y
通讯作者：
Yang Y
DOI：
10.1007/s10044-021-00984-y
发表时间：
2021
期刊：
Pattern analysis and applications : PAA
影响因子：
--
作者：
Narin A;Kaya C;Pamuk Z
通讯作者：
Pamuk Z
影响因子：
168.9
作者：
Huang, Chaolin;Wang, Yeming;Cao, Bin
通讯作者：
Cao, Bin
影响因子：
19.7
作者：
Li, Lin;Qin, Lixin;Xia, Jun
通讯作者：
Xia, Jun
DOI：
10.1016/j.ejrnm.2015.11.004
发表时间：
2015-11-25
期刊：
The Egyptian Journal of Radiology and Nuclear Medicine
影响因子：
--
作者：
Hamimi A
通讯作者：
Hamimi A