StackNet-DenVIS: a multi-layer perceptron stacked ensembling approach for COVID-19 detection using X-ray images.
StackNet-DenVIS: a multi-layer perceptron stacked ensembling approach for COVID-19 detection using X-ray images.
复制标题
DOI：
10.1007/s13246-020-00952-6
复制
发表时间：
2020-12
影响因子：
4.4
通讯作者：
Srivastava K
中科院分区：
文献类型：
--
作者：
Autee P;Bagwe S;Shah V;Srivastava K
2019冠状病毒病（COVID-19）的高度传染性导致全球大流行。由于COVID-19常规检测相对缓慢且繁重，因此需要一种更快的方法。目前的研究表明，在Covid-19阳性患者的胸部X光检查中发现的可见不规则现象表明存在这种疾病。因此，可以使用深度学习和图像分类技术来从这些不规则性中学习，并以高精度进行相应的分类。本研究提出了一种方法来创建一个名为StackNet-DenVIS的分类器模型，该模型旨在作为进行现有拭子测试之前的筛选过程。使用一种新的方法，它结合了迁移学习和堆叠泛化，该模型旨在降低分类的假阴性率，以补偿拭子测试的30%假阴性率。使用了从多个可靠来源收集的数据集，包括9953例胸部X光片（868例Covid和9085例非Covid）。此外，这项研究还展示了使用各种技术处理数据不平衡，包括生成对抗网络和采样技术。该模型的准确性、敏感性和特异性分别为95.07%、99.40%和94.61%。据我们所知，本文所获得的准确率和假阴性率的组合优于当前的实现。我们还必须强调，我们提出的架构也考虑了其他类型的病毒性肺炎。鉴于我们模型前所未有的灵敏度，我们乐观地认为它有助于更好地检测新冠肺炎。
The highly contagious nature of Coronavirus disease 2019 (Covid-19) resulted in a global pandemic. Due to the relatively slow and taxing nature of conventional testing for Covid-19, a faster method needs to be in place. The current researches have suggested that visible irregularities found in the chest X-ray of Covid-19 positive patients are indicative of the presence of the disease. Hence, Deep Learning and Image Classification techniques can be employed to learn from these irregularities, and classify accordingly with high accuracy. This research presents an approach to create a classifier model named StackNet-DenVIS which is designed to act as a screening process before conducting the existing swab tests. Using a novel approach, which incorporates Transfer Learning and Stacked Generalization, the model aims to lower the False Negative rate of classification compensating for the 30% False Negative rate of the swab tests. A dataset gathered from multiple reliable sources consisting of 9953 Chest X-rays (868 Covid and 9085 Non-Covid) was used. Also, this research demonstrates handling data imbalance using various techniques involving Generative Adversarial Networks and sampling techniques. The accuracy, sensitivity, and specificity obtained on our proposed model were 95.07%, 99.40% and 94.61% respectively. To the best of our knowledge, the combination of accuracy and false negative rate obtained by this paper outperforms the current implementations. We must also highlight that our proposed architecture also considers other types of viral pneumonia. Given the unprecedented sensitivity of our model we are optimistic it contributes to a better Covid-19 detection.
登录
查看更多内容
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：
10.6
作者：
Candemir, Sema;Jaeger, Stefan;McDonald, Clement J.
通讯作者：
McDonald, Clement J.
影响因子：
10.6
作者：
Shin HC;Roth HR;Gao M;Lu L;Xu Z;Nogues I;Yao J;Mollura D;Summers RM
通讯作者：
Summers RM
DOI：
10.33889/ijmems.2020.5.4.052
发表时间：
2020-08-01
影响因子：
1.6
作者：
Sethy, Prabira Kumar;Behera, Santi Kumari;Biswas, Preesat
通讯作者：
Biswas, Preesat
DOI：
10.3978/j.issn.2223-4292.2014.11.20
发表时间：
2014-12-01
影响因子：
2.8
作者：
Jaeger, Stefan;Candemir, Sema;Thoma, George
通讯作者：
Thoma, George