Detection of Covid-19 using Transfer Learning Technique

Nilesh Kumar, Atul Tripathi, Isha Pathak Tripathi
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Abstract

The whole world is suffering from the wave of the novel coronavirus that causes the large-scale death of a population and is proclaimed a pandemic by WHO. As RT-PCR tests to detect Coronavirus are costly and time taking. So now these days, the purpose of the researcher is to detect these diseases with the help of Artificial Intelligence or Machine learning-based models using CT scan images and X-rays images. So the testing cost, time taken and the number of data required could be minimized. In this paper, transfer learning based on three fine-tuned models has been proposed for Covid detection. The performance of these proposed fine-tuned models has been also compared with other competing models to check the accuracy and other matrices.
利用迁移学习技术检测Covid-19
世界卫生组织(WHO)宣布的新型冠状病毒感染症(covid - 19病毒)的大规模死亡正在席卷全球。因为检测冠状病毒的RT-PCR方法既昂贵又耗时。现在,研究人员的目的是借助人工智能或基于机器学习的模型,使用CT扫描图像和x射线图像来检测这些疾病。因此,测试成本、时间和所需数据的数量可以最小化。本文提出了基于三种微调模型的迁移学习方法用于新冠病毒检测。本文还将所提出的微调模型的性能与其他竞争模型进行了比较,以检查精度和其他矩阵。
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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