CapsNet-COVID19: Lung CT image classification method based on CapsNet model.

Abstract

The outbreak of the Corona Virus Disease 2019 (COVID-19) has posed a serious threat to human health and life around the world. As the number of COVID-19 cases continues to increase, many countries are facing problems such as errors in nucleic acid testing (RT-PCR), shortage of testing reagents, and lack of testing personnel. In order to solve such problems, it is necessary to propose a more accurate and efficient method as a supplement to the detection and diagnosis of COVID-19. This research uses a deep network model to classify some of the COVID-19, general pneumonia, and normal lung CT images in the 2019 Novel Coronavirus Information Database. The first level of the model uses convolutional neural networks to locate lung regions in lung CT images. The second level of the model uses the capsule network to classify and predict the segmented images. The accuracy of our method is 84.291% on the test set and 100% on the training set. Experiment shows that our classification method is suitable for medical image classification with complex background, low recognition rate, blurred boundaries and large image noise. We believe that this classification method is of great value for monitoring and controlling the growth of patients in COVID-19 infected areas.