A survey on deep learning models for detection of COVID-19.

IF 4.5 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Javad Mozaffari, Abdollah Amirkhani, Shahriar B Shokouhi
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引用次数: 2

Abstract

The spread of the COVID-19 started back in 2019; and so far, more than 4 million people around the world have lost their lives to this deadly virus and its variants. In view of the high transmissibility of the Corona virus, which has turned this disease into a global pandemic, artificial intelligence can be employed as an effective tool for an earlier detection and treatment of this illness. In this review paper, we evaluate the performance of the deep learning models in processing the X-Ray and CT-Scan images of the Corona patients' lungs and describe the changes made to these models in order to enhance their Corona detection accuracy. To this end, we introduce the famous deep learning models such as VGGNet, GoogleNet and ResNet and after reviewing the research works in which these models have been used for the detection of COVID-19, we compare the performances of the newer models such as DenseNet, CapsNet, MobileNet and EfficientNet. We then present the deep learning techniques of GAN, transfer learning, and data augmentation and examine the statistics of using these techniques. Here, we also describe the datasets introduced since the onset of the COVID-19. These datasets contain the lung images of Corona patients, healthy individuals, and the patients with non-Corona pulmonary diseases. Lastly, we elaborate on the existing challenges in the use of artificial intelligence for COVID-19 detection and the prospective trends of using this method in similar situations and conditions.

Supplementary information: The online version contains supplementary material available at 10.1007/s00521-023-08683-x.

Abstract Image

Abstract Image

Abstract Image

新冠肺炎检测的深度学习模型调查。
新冠肺炎的传播始于2019年;到目前为止,全世界已有400多万人死于这种致命的病毒及其变种。鉴于冠状病毒的高传播性已将这种疾病转变为全球大流行,人工智能可以作为早期检测和治疗这种疾病的有效工具。在这篇综述文章中,我们评估了深度学习模型在处理Corona患者肺部的X射线和CT扫描图像方面的性能,并描述了对这些模型所做的更改,以提高其Corona检测的准确性。为此,我们介绍了著名的深度学习模型,如VGGNet、GoogleNet和ResNet,并在回顾了这些模型用于检测新冠肺炎的研究工作后,比较了DenseNet、CapsNet、MobileNet和EfficientNet等新模型的性能。然后,我们介绍了GAN、迁移学习和数据扩充的深度学习技术,并检查了使用这些技术的统计数据。在这里,我们还描述了自新冠肺炎爆发以来引入的数据集。这些数据集包含Corona患者、健康人和非Corona肺部疾病患者的肺部图像。最后,我们阐述了在使用人工智能检测新冠肺炎方面存在的挑战,以及在类似情况和条件下使用这种方法的预期趋势。补充信息:在线版本包含补充材料,可访问10.1007/s00521-023-08683-x。
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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来源期刊
Neural Computing & Applications
Neural Computing & Applications 工程技术-计算机:人工智能
CiteScore
11.40
自引率
8.30%
发文量
1280
审稿时长
6.9 months
期刊介绍: Neural Computing & Applications is an international journal which publishes original research and other information in the field of practical applications of neural computing and related techniques such as genetic algorithms, fuzzy logic and neuro-fuzzy systems. All items relevant to building practical systems are within its scope, including but not limited to: -adaptive computing- algorithms- applicable neural networks theory- applied statistics- architectures- artificial intelligence- benchmarks- case histories of innovative applications- fuzzy logic- genetic algorithms- hardware implementations- hybrid intelligent systems- intelligent agents- intelligent control systems- intelligent diagnostics- intelligent forecasting- machine learning- neural networks- neuro-fuzzy systems- pattern recognition- performance measures- self-learning systems- software simulations- supervised and unsupervised learning methods- system engineering and integration. Featured contributions fall into several categories: Original Articles, Review Articles, Book Reviews and Announcements.
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