用于 COVID-19 检测的深度学习模型/技术:调查

Kumari Archana, Amandeep Kaur, Yonis Gulzar, Yasir Hamid, Mohammad Shuaib Mir, Arjumand Bano Soomro
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引用次数: 0

摘要

COVID-19 的早期检测和初步诊断在有效控制该流行病方面发挥着至关重要的作用。放射影像已成为实现这一目标的重要工具。深度学习技术是人工智能的一个子集,已被广泛用于处理和分析这些放射影像。值得注意的是,深度学习技术识别和检测放射影像中模式的能力已超越 COVID-19,可用于识别与其他流行病或疾病相关的模式。本文旨在概述基于放射学数据(X 射线和 CT 图像)开发的用于检测电晕病毒(COVID-19)的深度学习技术。本文还介绍了该领域中用于特征提取和数据预处理的方法。本研究的目的是让研究人员更容易理解用于检测 COVID-19 的各种深度学习技术,并引入或整合这些方法,以防止未来电晕病毒的传播。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep learning models/techniques for COVID-19 detection: a survey
The early detection and preliminary diagnosis of COVID-19 play a crucial role in effectively managing the pandemic. Radiographic images have emerged as valuable tool in achieving this objective. Deep learning techniques, a subset of artificial intelligence, have been extensively employed for the processing and analysis of these radiographic images. Notably, their ability to identify and detect patterns within radiographic images can be extended beyond COVID-19 and can be applied to recognize patterns associated with other pandemics or diseases. This paper seeks to provide an overview of the deep learning techniques developed for detection of corona-virus (COVID-19) based on radiological data (X-Ray and CT images). It also sheds some information on the methods utilized for feature extraction and data preprocessing in this field. The purpose of this study is to make it easier for researchers to comprehend various deep learning techniques that are used to detect COVID-19 and to introduce or ensemble those approaches to prevent the spread of corona virus in future.
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