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Transfer Learning from Pneumonia to COVID-19

机译:从肺炎到Covid-19转移学习

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摘要

Developing an intelligent application to assist the detection and study of the COVID-19 infection is crucial and urgent during this pandemic, given the scarcity of available data and the rapidly changing virus. This paper presents a study of transfer learning in image classification to efficiently develop deep learning models following a three-stage procedure to take advantage of pre-trained models from one area and effectively modify the model for application in a relatively new area. The case study in this work is the classification of COVID-19 X-ray images. The experiment evaluations show that the proposed method and developed models achieve satisfactory results in a timely manner.
机译:开发智能申请以协助对Covid-19感染的检测和研究在这种大流行中至关重要,鉴于可用数据的稀缺和快速变化的病毒,这是至关重要的。 本文提出了在图像分类中转移学习的研究,以便在三阶段过程中有效开发深度学习模型,从一个区域利用预先训练的模型,并有效地修改了在相对较新的区域中的应用模型。 在这项工作中的案例研究是Covid-19 X射线图像的分类。 实验评估表明,该方法和开发的模型及时实现了令人满意的结果。

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