COVSeg-NET: A deep convolution neural network for COVID-19 lung CT image segmentation

XiaoQing Zhang; GuangYu Wang; Shu-Guang Zhao

doi:10.1002/ima.22611

COVSeg-NET: A deep convolution neural network for COVID-19 lung CT image segmentation

Int J Imaging Syst Technol. 2021 Sep;31(3):1071-1086. doi: 10.1002/ima.22611. Epub 2021 Jun 4.

Authors

XiaoQing Zhang¹, GuangYu Wang², Shu-Guang Zhao²

Affiliations

¹ Taizhou Institute of Science and Technology, Nanjing University of Science and Technology No.8, Meilan East Road Taizhou China.
² College of Information Science and Technology, Donghua University Shanghai China.

Abstract

COVID-19 is a new type of respiratory infectious disease that poses a serious threat to the survival of human beings all over the world. Using artificial intelligence technology to analyze lung images of COVID-19 patients can achieve rapid and effective detection. This study proposes a COVSeg-NET model that can accurately segment ground glass opaque lesions in COVID-19 lung CT images. The COVSeg-NET model is based on the fully convolutional neural network model structure, which mainly includes convolutional layer, nonlinear unit activation function, maximum pooling layer, batch normalization layer, merge layer, flattening layer, sigmoid layer, and so forth. Through experiments and evaluation results, it can be seen that the dice coefficient, sensitivity, and specificity of the COVSeg-NET model are 0.561, 0.447, and 0.996 respectively, which are more advanced than other deep learning methods. The COVSeg-NET model can use a smaller training set and shorter test time to obtain better segmentation results.

Keywords: COVID‐19; convolution neural network; image segmentation; lung CT image.