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Deep Learning Applications for COVID-19: A Brief Review

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Research and Education: Traditions and Innovations (INTER-ACADEMIA 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 422))

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Abstract

With the emergence of Coronavirus Disease 2019 (COVID-19) and millions of confirmed cases over the world, fast detection with low diagnosis error has become a pivotal task among the research community. Owing to image processing based methodologies in Artificial Intelligence (AI), the chest X-ray images along with Deep Learning (DL) algorithms have recently become a valid choice for early COVID-19 screening. This review has scanned well-known scientific databases based on the target intervention (DL) and the target population (COVID-19). This study retrieved 60 studies, after passing through excluding criteria, only 25 studies are considered in this brief review. Due to the need for a reference for the use of DL in the healthcare domain, this review paper has tried to provide a nutshell resource for researchers to think about the design of more effective Deep Learning models for early COVID-19 detection. Although the majority of the Deep learning methods are still in development and not tested in a clinical setting, the included studies in this literature showed that using Deep learning models can impact the detection of COVID-19 with an acceptable rate of accuracy.

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Authors and Affiliations

  1. Department of Computer Science, Faculty of Mathematical Sciences, University of Tabriz, Tabriz, Iran

    Hamed Tabrizchi & Jafar Razmara

  2. John von Neumann Faculty of Informatics, Obuda University, Budapest, Hungary

    Amir Mosavi

  3. Institute of Software Design and Software Development, Obuda University, Budapest, Hungary

    Annamaria R. Varkonyi-Koczy

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  1. Hamed Tabrizchi
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  2. Jafar Razmara
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  3. Amir Mosavi
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  4. Annamaria R. Varkonyi-Koczy
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Correspondence to Hamed Tabrizchi .

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Editors and Affiliations

  1. Francisk Skorina Gomel State University, Gomel, Belarus

    Sergei Khakhomov

  2. Francisk Skorina Gomel State University, Gomel, Belarus

    Igor Semchenko

  3. Francisk Skorina Gomel State University, Gomel, Belarus

    Oleg Demidenko

  4. Dean of the Faculty of Physics and Information Technologies, Francisk Skorina Gomel State University, Gomel, Belarus

    Dmitry Kovalenko

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Tabrizchi, H., Razmara, J., Mosavi, A., Varkonyi-Koczy, A.R. (2022). Deep Learning Applications for COVID-19: A Brief Review. In: Khakhomov, S., Semchenko, I., Demidenko, O., Kovalenko, D. (eds) Research and Education: Traditions and Innovations. INTER-ACADEMIA 2021. Lecture Notes in Networks and Systems, vol 422. Springer, Singapore. https://doi.org/10.1007/978-981-19-0379-3_12

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