Deep learning based on model migration for COVID-19 identification

Abstract

Since the end of 2019, the virus has gradually spread and eventually spread globally. In this context, it is important to control the spread of COVID-19 quickly. This project attempts to use artificial intelligence to identify CT images of the lungs of COVID-19 patients and facilitate rapid screening of COVID-19 patients. The main focus of this study is to use artificial intelligence based on model transfer deep learning to identify whether patients are infected with novel coronavirus through patient lung images. The difficulty of this task is that the number of lung images of COVID-19 patients is very limited, which makes it very difficult to train traditional neural networks. Traditional computer vision deep learning to extract image features requires a large number of sample data for model training. If the number of images in the data set is too small, the model will overfit and fail to achieve relatively accurate COVID-19 identification effect. To solve the above problems, this paper studied novel coronavirus identification of patients' lung CT images by deep learning method based on model transfer. We build models based on similar types of problems, store those models and then fine-tune them. Eventually, a model was trained to recognize images of the lungs of COVID-19 patients. The method was tested on publicly available COVID-19 datasets, and the results showed that the identification accuracy of the method was about 70%.