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A review of Deep learning Techniques for COVID-19 identification on Chest CT images

arXiv 2208.00032 ↗First posted 29 Jul 2022eess.IV cs.CV cs.LGcited by 0

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Abstract (arXiv)

The current COVID-19 pandemic is a serious threat to humanity that directly affects the lungs. Automatic identification of COVID-19 is a challenge for health care officials. The standard gold method for diagnosing COVID-19 is Reverse Transcription Polymerase Chain Reaction (RT-PCR) to collect swabs from affected people. Some limitations encountered while collecting swabs are related to accuracy and longtime duration. Chest CT (Computed Tomography) is another test method that helps healthcare providers quickly identify the infected lung areas. It was used as a supporting tool for identifying COVID-19 in an earlier stage. With the help of deep learning, the CT imaging characteristics of COVID-19. Researchers have proven it to be highly effective for COVID-19 CT image classification. In this study, we review the recent deep learning techniques that can use to detect the COVID-19 disease. Relevant studies were collected by various databases such as Web of Science, Google Scholar, and PubMed. Finally, we compare the results of different deep learning models, and CT image analysis is discussed.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 9 of its 53 references that are in the map. First posted on arXiv 29 Jul 2022.

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References · 9

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  5. 2020COVID-CT-Dataset: A CT Scan Dataset about COVID-19cited by 93
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  7. 2020Lung Infection Quantification of COVID-19 in CT Images with Deep Learningcited by 28
  8. 2020Automated Detection and Forecasting of COVID-19 using Deep Learning Techniques: A Reviewcited by 21
  9. 2020Coronavirus Detection and Analysis on Chest CT with Deep Learningcited by 5

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