应用迁移学习方法识别新冠肺炎患者肺炎症状

P M Ebin, B. Athira
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引用次数: 0

摘要

全球有超过100万人受到COVID - 19流行病的影响,该流行病还夺去了100多万人的生命。Covid - 19感染的结果可能是肺炎,使患者面临严重疾病甚至死亡的危险。因此,在Covid - 19患者中识别肺炎的迹象及其存在至关重要。VGG16架构是一种深度学习架构,是2014年视觉识别挑战赛的亚军。研究人员正在应用迁移学习来检测这种情况下是否存在肺炎。来自kaggle(一个可公开访问的开放数据集)的胸部x射线扫描作为该研究的数据集。该模型的准确率为95.83%,并与其他各种模型进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identification of Pneumonia Symptoms in Covid19 patients using Transfer Learning Approach
Over 1 million individuals were impacted globally by the COVID 19 epidemic, which also claimed over 10 lakh lives. As a result of the Covid 19 infection, pneumonia might develop, putting the patient in danger of serious illness or even death. Therefore, it is crucial to recognize the signs of pneumonia and its existence in Covid 19 patients. The VGG16 architecture is a Deep Learning architecture that was the first runner-up in the 2014 visual recognition challenge. The researchers are applying transfer-learning to detect the presence of pneumonia in this case. Chest X-ray scans from kaggle, a publicly accessible open dataset, served as the study’s data set. The model’s accuracy was 95.83%, and a comparison with various other models was also presented.
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