A Survey on Deep Transfer Learning to Edge Computing for Mitigating the COVID-19 Pandemic

Sufian, Abu; Ghosh, Anirudha; Sadiq, Ali Safaa; Smarandache, Florentín

doi:10.36227/techrxiv.12535010.v4

Cited by 10 publications

(13 citation statements)

References 2 publications

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“…Also, examinations are rarely performed in community health centers or hospitals due to a lack of funding and space compared to the large number of cases seen at any given time. To deal with such a scenario, researchers in this field are working hard to build some potential TL mechanisms that can help mitigate the problems [ 52 ].…”

Section: Basic Concepts and Corresponding Terminologiesmentioning

confidence: 99%

“…Finally, Sufian, Ghosh [ 52 ] discussed the benefits and drawbacks of deep TL approaches, edge computing, and related problems in combating the COVID-19 epidemic. Also, they introduced a hypothetical combined model, outlining the context and potential implications of working at sensitive sites with real data.…”

Section: Relevant Reviewsmentioning

confidence: 99%

“… •There has not been any in-depth review of the papers. Sufian, Ghosh [ 52 ] Examining Covid-19's deep TL methods. •In the context of COVID-19, brief evaluations and challenges have been given.…”

Section: Relevant Reviewsmentioning

confidence: 99%

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The COVID-19 epidemic analysis and diagnosis using deep learning: A systematic literature review and future directions

Heidari¹,

Navimipour²,

Ünal³

et al. 2022

Computers in Biology and Medicine

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Since December 2019, the COVID-19 outbreak has resulted in countless deaths and has harmed all facets of human existence. COVID-19 has been designated an epidemic by the World Health Organization (WHO), which has placed a tremendous burden on nearly all countries, especially those with weak health systems. However, Deep Learning (DL) has been applied in several applications and many types of detection applications in the medical field, including thyroid diagnosis, lung nodule recognition, fetal localization, and detection of diabetic retinopathy. Furthermore, various clinical imaging sources, like Magnetic Resonance Imaging (MRI), X-ray, and Computed Tomography (CT), make DL a perfect technique to tackle the epidemic of COVID-19. Inspired by this fact, a considerable amount of research has been done. A Systematic Literature Review (SLR) has been used in this study to discover, assess, and integrate findings from relevant studies. DL techniques used in COVID-19 have also been categorized into seven main distinct categories as Long Short Term Memory Networks (LSTM), Self-Organizing Maps (SOMs), Conventional Neural Networks (CNNs), Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs), Autoencoders, and hybrid approaches. Then, the state-of-the-art studies connected to DL techniques and applications for health problems with COVID-19 have been highlighted. Moreover, many issues and problems associated with DL implementation for COVID-19 have been addressed, which are anticipated to stimulate more investigations to control the prevalence and disaster control in the future. According to the findings, most papers are assessed using characteristics such as accuracy, delay, robustness, and scalability. Meanwhile, other features are underutilized, such as security and convergence time. Python is also the most commonly used language in papers, accounting for 75% of the time. According to the investigation, 37.83% of applications have identified chest CT/chest X-ray images for patients.

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Section: Basic Concepts and Corresponding Terminologiesmentioning

confidence: 99%

Section: Relevant Reviewsmentioning

confidence: 99%

See 1 more Smart Citation

The COVID-19 epidemic analysis and diagnosis using deep learning: A systematic literature review and future directions

Heidari¹,

Navimipour²,

Ünal³

et al. 2022

Computers in Biology and Medicine

View full text Add to dashboard Cite

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“…A comprehensive survey by Sufian et al [11] This model reportedly achieves better classification accuracies compared to human experts. Similarly, Zhang et al [13] proposed a two-stage deep learning framework for COVID19 management with CT lung images.…”

Section: Deep Learningmentioning

confidence: 99%

Deep Transfer Learning Based Unified Framework for COVID19 Classification and Infection Detection from Chest X-Ray Images

et al. 2021

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The presentation of the COVID19 has endangered several million lives worldwide causing thousands of deaths every day. Evolution of COVID19 as a pandemic calls for automated solutions for initial screening and treatment management. In addition to the thermal scanning mechanisms, findings from chest X-ray imaging examinations are reliable predictors in COVID19 detection, long-term monitoring and severity evaluation. This paper presents a novel deep transfer learning based framework for COVID19 detection and segmentation of infections from chest X-ray images. It is realized as a two-stage cascaded framework with classifier and segmentation subnetwork models. The classifier is modeled as a fine-tuned residual SqueezeNet network, and the segmentation network is implemented as a fine-tuned SegNet semantic segmentation network. The segmentation task is enhanced with a bioinspired Gaussian Mixture Model-based super pixel segmentation. This framework is trained and tested with two public datasets for binary and multiclass classifications and infection segmentation. It achieves accuracies of 99.69% and 99.48% for binary and three class classifications, and a mean accuracy of 83.437% for segmentation. Experimental results and comparative evaluations demonstrate the superiority of this unified model and signify potential extensions for biomarker definition and severity quantization.

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“…Furthermore, the use of popular AI techniques such as neural systems, classical SVM, and edge significant learning battling COVID-19 is analyzed in [30] . Meanwhile, a survey paper in [31] provides a systematic study of deep learning, deep transfer learning and edge computing to mitigate COVID-19. Other works in [32] , [33] also survey the applications of AI techniques in screening, tracking, and predicting the spread of COVID-19.…”

Section: Introductionmentioning

confidence: 99%

Blockchain and AI-Based Solutions to Combat Coronavirus (COVID-19)-Like Epidemics: A Survey

et al. 2021

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The beginning of 2020 has seen the emergence of coronavirus outbreak caused by a novel virus called SARS-CoV-2. The sudden explosion and uncontrolled worldwide spread of COVID-19 show the limitations of existing healthcare systems to timely handle public health emergencies. In such contexts, innovative technologies such as blockchain and Artificial Intelligence (AI) have emerged as promising solutions for fighting coronavirus epidemic. Blockchain can combat pandemics by enabling early detection of outbreaks, ensuring the ordering of medical data, and ensuring reliable medical supply chain during the outbreak tracing. Moreover, AI provides intelligent solutions for identifying symptoms caused by coronavirus for treatments and supporting drug manufacturing. Motivated by these, in this paper we present an extensive survey on the use of blockchain and AI for combating COVID-19 epidemics. First, we introduce a new conceptual architecture which integrates blockchain and AI for fighting COVID-19 . Then, we survey the latest research efforts on the use of blockchain and AI for fighting COVID-19 in various applications. The newly emerging projects and use cases enabled by these technologies to deal with coronavirus pandemic are also presented. A case study is also provided using federated AI for COVID-19 detection. Finally, we point out challenges and future directions that motivate more research efforts to deal with future coronaviruslike epidemics.

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A Survey on Deep Transfer Learning to Edge Computing for Mitigating the COVID-19 Pandemic

Cited by 10 publications

References 2 publications

The COVID-19 epidemic analysis and diagnosis using deep learning: A systematic literature review and future directions

The COVID-19 epidemic analysis and diagnosis using deep learning: A systematic literature review and future directions

Deep Transfer Learning Based Unified Framework for COVID19 Classification and Infection Detection from Chest X-Ray Images

Blockchain and AI-Based Solutions to Combat Coronavirus (COVID-19)-Like Epidemics: A Survey

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