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COVSeg‐NET : A deep convolution neural network for COVID ‐19 lung CT image segmentation
Author(s) -
Zhang XiaoQing,
Wang GuangYu,
Zhao ShuGuang
Publication year - 2021
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.22611
Subject(s) - computer science , convolutional neural network , artificial intelligence , segmentation , normalization (sociology) , merge (version control) , sørensen–dice coefficient , test set , artificial neural network , covid-19 , pattern recognition (psychology) , deep learning , image segmentation , medicine , pathology , infectious disease (medical specialty) , disease , information retrieval , sociology , anthropology
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.