%0 Journal Article
%A CHANG Chun
%A HE Zhiqiang
%A LIU Kexin
%A NIU Kai
%A HUANG Liuting
%T Automatic Recognition of Mucus Impaction in CT Images of Asthmatic Patients Using Deep Learning
%D 2022
%R 10.13190/j.jbupt.2022-030
%J Journal of Beijing University of Posts and Telecommunications
%P 58-63
%V 45
%N 4
%X In view of the low efficiency of manual identification of mucus impaction in chest computed tomography (CT) and the poor recognition effect, a deep neural network based automatic recognition algorithm for mucus impaction is proposed. In order to deal with the irregular characteristics of mucus impaction, deformable convolution is added to the backbone to extract features, and deformable region of interest pooling is used in the detection network to normalize the feature scale. Besides, feature pyramid network with weight coefficient is used for multi-scale fusion according to the characteristics of small and medium objects. The results show that compared with the traditional faster region convolutional neural network, mean average precision of the proposed algorithm is improved by 4%, which can provide auxiliary reference for the diagnosis of asthma severity.
%U https://journal.bupt.edu.cn/EN/10.13190/j.jbupt.2022-030