Deep learning techniques for detection of covid-19 using chest x-rays

A. Naveen, B. Manoj, G. Akhila, M. B. Nakarani, J. Sreekar, P. Beriwal, N. Gupta, S. Narayanan
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引用次数: 1

Abstract

The COVID-19 pandemic situation keeps on ruining and affecting the wellbeing and prosperity of the worldwide population and due to this situation, the doctors around the world are working restlessly, as the coronavirus is increasing exponentially and the situation for testing has become quite a problematic and with restricted testing units, it’s impossible for every patient to be tested with available facilities. Effective screening of infected patients through chest X-ray images is a critical step in combating COVID-19. With the help of deep learning techniques, it is possible to train various radiology images and detect COVID-19. The dataset used in our research work is gathered from different sources and a specific new dataset is generated. The proposed methodology implemented is beneficial to the medical practitioner for the diagnosis of coronavirus infected patients where predictions can be done automated using deep learning. The deep learning algorithms that are used to predict the COVID with the help of chest X-ray images are evaluated for their prediction based on performance metrics such as accuracy, precision, Recall, and F1-score. In this work, the proposed model has used deep learning techniques for COVID-19 prediction and the results have shown superior performance in prediction of COVID-19. © 2021, International Institute for General Systems Studies. All rights reserved.
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利用胸部x光检测新冠肺炎的深度学习技术
新冠肺炎疫情持续破坏和影响全球人口的福祉和繁荣,由于这种情况,世界各地的医生都在不安地工作,因为冠状病毒呈指数级增长,检测情况变得相当困难,检测单位受到限制,不可能用现有的设施对每个病人进行检测。通过胸部X光图像对感染患者进行有效筛查是抗击新冠肺炎的关键一步。借助深度学习技术,可以训练各种放射学图像并检测新冠肺炎。我们研究工作中使用的数据集是从不同的来源收集的,并生成了一个特定的新数据集。所实施的拟议方法有利于医生诊断冠状病毒感染的患者,其中可以使用深度学习自动进行预测。用于借助胸部X射线图像预测新冠肺炎的深度学习算法基于准确性、精确度、召回率和F1分数等性能指标进行预测评估。在这项工作中,所提出的模型将深度学习技术用于新冠肺炎预测,结果在新冠肺炎预测中显示出优异的性能。©2021,国际通用系统研究所。保留所有权利。
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来源期刊
Advances in Systems Science and Applications
Advances in Systems Science and Applications Engineering-Engineering (all)
CiteScore
1.20
自引率
0.00%
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0
期刊介绍: Advances in Systems Science and Applications (ASSA) is an international peer-reviewed open-source online academic journal. Its scope covers all major aspects of systems (and processes) analysis, modeling, simulation, and control, ranging from theoretical and methodological developments to a large variety of application areas. Survey articles and innovative results are also welcome. ASSA is aimed at the audience of scientists, engineers and researchers working in the framework of these problems. ASSA should be a platform on which researchers will be able to communicate and discuss both their specialized issues and interdisciplinary problems of systems analysis and its applications in science and industry, including data science, artificial intelligence, material science, manufacturing, transportation, power and energy, ecology, corporate management, public governance, finance, and many others.
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