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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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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