Artificial Intelligence and Deep Learning Assisted Rapid Diagnosis of COVID-19 from Chest Radiographical Images: A Survey.
Artificial Intelligence and Deep Learning Assisted Rapid Diagnosis of COVID-19 from Chest Radiographical Images: A Survey.
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DOI：
10.1155/2022/1306664
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发表时间：
2022
影响因子：
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通讯作者：
Agrawal, Sanjay
中科院分区：
文献类型：
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作者：
Sinwar, Deepak;Dhaka, Vijaypal Singh;Tesfaye, Biniyam Alemu;Raghuwanshi, Ghanshyam;Kumar, Ashish;Maakar, Sunil Kr;Agrawal, Sanjay
人工智能（AI）已经成功地应用于许多现实生活领域，用于解决复杂问题。随着机器学习（ML）范式的发明，研究人员可以方便地根据过去的数据预测结果。如今，机器学习在早期发现有症状的病例，并警告人们未来的影响，成为应对新冠肺炎疫情的最大武器。观察到，COVID-19在短时间内在全球范围内爆发的原因是检测设施短缺和检测报告延迟。为了应对这一挑战，人工智能可以有效地应用于生产快速且具有成本效益的解决方案。许多研究人员提出了基于人工智能的解决方案，用于利用胸部CT图像进行初步诊断、呼吸声音分析、有症状者与无症状者的声音分析等。一些基于人工智能的应用程序声称在预测新冠病毒阳性几率方面具有很高的准确性。在短时间内，关于COVID-19的鉴定发表了大量的研究工作。本文对来自施普林格、IEEE、Elsevier、MDPI、arXiv和medRxiv等知名来源的110多篇论文进行了仔细研究和全面调查。本次调查选择的大多数论文都介绍了利用基于深度学习的胸部x光片和CT扫描图像模型来检测和分类COVID-19的坦率工作。我们希望这项调查涵盖了大部分工作，并为研究界提出有效和准确的解决方案提供见解，以应对这一流行病。
Artificial Intelligence (AI) has been applied successfully in many real-life domains for solving complex problems. With the invention of Machine Learning (ML) paradigms, it becomes convenient for researchers to predict the outcome based on past data. Nowadays, ML is acting as the biggest weapon against the COVID-19 pandemic by detecting symptomatic cases at an early stage and warning people about its futuristic effects. It is observed that COVID-19 has blown out globally so much in a short period because of the shortage of testing facilities and delays in test reports. To address this challenge, AI can be effectively applied to produce fast as well as cost-effective solutions. Plenty of researchers come up with AI-based solutions for preliminary diagnosis using chest CT Images, respiratory sound analysis, voice analysis of symptomatic persons with asymptomatic ones, and so forth. Some AI-based applications claim good accuracy in predicting the chances of being COVID-19-positive. Within a short period, plenty of research work is published regarding the identification of COVID-19. This paper has carefully examined and presented a comprehensive survey of more than 110 papers that came from various reputed sources, that is, Springer, IEEE, Elsevier, MDPI, arXiv, and medRxiv. Most of the papers selected for this survey presented candid work to detect and classify COVID-19, using deep-learning-based models from chest X-Rays and CT scan images. We hope that this survey covers most of the work and provides insights to the research community in proposing efficient as well as accurate solutions for fighting the pandemic.
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影响因子：
3
作者：
Bzhalava Z;Tampuu A;Bała P;Vicente R;Dillner J
通讯作者：
Dillner J
影响因子：
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作者：
Aftab M;Amin R;Koundal D;Aldabbas H;Alouffi B;Iqbal Z
通讯作者：
Iqbal Z
DOI：
10.3390/healthcare9050522
发表时间：
2021-04-29
期刊：
Healthcare (Basel, Switzerland)
影响因子：
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作者：
Almalki YE;Qayyum A;Irfan M;Haider N;Glowacz A;Alshehri FM;Alduraibi SK;Alshamrani K;Alkhalik Basha MA;Alduraibi A;Saeed MK;Rahman S
通讯作者：
Rahman S
影响因子：
2.3
作者：
Ardabili, Sina F.;Mosavi, Amir;Atkinson, Peter M.
通讯作者：
Atkinson, Peter M.
分析重症监护术中199名患者的临床表现和成像。
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