el 22(23): e3

Research Article

Covid-19 Recognition by Chest CT and Deep Learning

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  • @ARTICLE{10.4108/eai.7-1-2022.172812,
        author={Lin Yang and Dimas Lima},
        title={Covid-19 Recognition by Chest CT and Deep Learning},
        journal={EAI Endorsed Transactions on e-Learning},
        volume={7},
        number={23},
        publisher={EAI},
        journal_a={EL},
        year={2022},
        month={1},
        keywords={Covid-19, deep learning, deep transfer learning, ResNet152V2},
        doi={10.4108/eai.7-1-2022.172812}
    }
    
  • Lin Yang
    Dimas Lima
    Year: 2022
    Covid-19 Recognition by Chest CT and Deep Learning
    EL
    EAI
    DOI: 10.4108/eai.7-1-2022.172812
Lin Yang1,*, Dimas Lima2
  • 1: School of Computing and Mathematical Sciences, The University of Leicester, University Road, LE1 7RH, United Kingdom
  • 2: Department of Electrical Engineering, Federal University of Santa Catarina, 88040-900, Florian√≥polis, Brazil
*Contact email: ylin131@gmail.com

Abstract

INTRODUCTION: The current RT-qPCR approach to identify Covid-19 diseases is slow and non-optimal for a large number of candidates.

OBJECTIVES: Several studies have demonstrated that deep learning can help healthcare professionals diagnose Covid-19 patients. The deep learning model proposed in this paper significantly enhanced the accuracy of identifying Covid-19 patients compared to prior approaches.

METHODS: This paper applies transfer learning and deep residual network ResNet152V2 to detect Covid-19 patients with the help of CT scan images. Monte Carlo Cross-Validation has been applied to obtain an accurate and valid result.

RESULTS: The proposed model can identify Covid-19 disease with an overall accuracy of 95.06%, along with an average precision and recall of 97.19% and 92.81%, respectively. It also obtained a specificity of 93.14% and a F1-score of 94.96%.

CONCLUSION: The performance of this proposed ResNet152V2 model is superior to most of the current Covid-19 detection models.