Transfer Learning from Pneumonia to COVID-19

Hongen Lu, Sandini Anuradha Hewakankanamge, Yuan Miao
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引用次数: 2

Abstract

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.
将学习从肺炎转移到COVID-19
鉴于现有数据的缺乏和病毒的迅速变化,在本次大流行期间,开发一款智能应用程序以协助检测和研究COVID-19感染至关重要且紧迫。本文研究了图像分类中的迁移学习,以有效地开发深度学习模型,遵循三个阶段的过程,利用来自一个领域的预训练模型,并有效地修改模型以应用于相对较新的领域。本工作的案例研究是COVID-19 x射线图像的分类。实验结果表明,所提出的方法和所建立的模型及时地取得了令人满意的结果。
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