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A New Classification Model Based on Stacknet and Deep Learning for Fast Detection of COVID 19 Through X Rays Images

机译:基于Stacknet的新分类模型,深度学习,用于快速检测Covid 19到X射线图像

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Coronavirus (COVID-19) is continuing its spread across the world, with more than seven million confirmed cases. The findings could be important as lockdown restrictions begin to be eased, and they highlight the need for the introduction of increasingly effective techniques to deal with this spread and help effectively identify new infections more quickly, at a reasonable cost and with a minimum error rate. The use of machine learning models constitutes a new approach, used more and more in this field. In this proposed work, we built a new classification model named CovStacknet and it based on StackNet metamodeling methodology combined with deep convolutional neural network as the basis for features extraction from XRay images. The proposed model has reached an accuracy score of 98%, which is better than that achieved by the basic models.
机译:Coronavirus(Covid-19)正在继续遍布全球,拥有超过700万个确认的案例。结果可能是重要的,因为锁定限制开始被削弱,并且它们突出了引入越来越有效的技术来处理这种扩展的必要性,并以合理的成本和最小的错误率有效地更快地识别新的感染。机器学习模型的使用构成了一种新方法,在这一领域使用了越来越多的方法。在这一拟议的工作中,我们建立了一个名为Covstacknet的新分类模型,它基于StackNet Metamodeling方法,与深卷积神经网络相结合,作为来自X射线图像的特征提取的基础。拟议的模型达到了98%的准确度分数,比基本模型所实现的更好。

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