A Survey on how computer vision can response to urgent need to contribute in COVID-19 pandemics

S. Gazzah, O. Bencharef
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引用次数: 11

Abstract

The coronavirus first outbreak in Wuhan city of China by December 2019. Due to its highly contagious power, they spread promptly in the four continents. Moreover, it devastating our daily lives and cause huge economic damage. Therefore, it is urgent to detect the positive cases at the earliest and put then under isolation. Automatic virus detection using Machine Learning will be a valuable contribution to prevent the spread of this epidemic. The purpose of this paper is to present short reviews on the coronavirus detection. In reviewing the existing works, we summarized and compared some related works performed on a collection of CT and X-ray images provided from infected patients. We conclude the paper with some discussions on how computer vision can response to urgent need to contribute in pandemics and to investigate many aspects of new viral replication and pathogenesis.
计算机视觉如何应对COVID-19大流行的迫切需求
2019年12月,冠状病毒首次在中国武汉市爆发。由于其高度传染性,它们迅速在四大洲传播。此外,它破坏了我们的日常生活,造成巨大的经济损失。因此,尽早发现阳性病例并对其进行隔离是当务之急。使用机器学习的自动病毒检测将是防止这种流行病传播的宝贵贡献。本文的目的是对冠状病毒的检测进行简要综述。在回顾已有工作的基础上,我们总结并比较了一些对感染患者提供的CT和x线图像进行的相关工作。最后,我们讨论了计算机视觉如何响应流行病的迫切需要,以及如何研究新病毒复制和发病机制的许多方面。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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