Covid-19 Recognition by Chest CT and Deep Learning - Lin Yang; Dimas Lima (European Alliance for Innovation (EAI), 2022)
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.