Abstract

Info

Author(s):: GUO Yi1; DU Qiuchen2; WU Mengmeng3; MA Pengtao1; LI Guanhua1; 1. Department of Anesthesiology, PLA Rocket Force Characteristic Medical Center, Beijing 100088, China 2.School of Electronic Information Engineering, Beihang University, Beijing 100191, China 3.Department of Imaging,PLA Rocket Force Characteristic Medical Center, Beijing 100088, China

Keywords:: Keywords: corona virus disease 2019 computed tomography lightweight network recognition technology GhostNet

Abstract:: Abstract: Objective To propose a novel corona virus disease 2019 (COVID-19) recognition technology based on lightweight neural network for meeting the actual needs of COVID-19 detection in clinic. Methods All public COVID-19 CT image data set were selected and taken as training data after gray-level normalization and data cleaning. The generalization ability of deep learning was improved by large sample. Then the lightweight network GhostNet was adopted to simplify the network parameters, so that the deep learning model could run on medical computer and improved the efficiency of COVID-19 diagnosis based on CT. Subsequently, the diagnostic accuracy was further improve by adding lung image segmentation to network input. Finally, a weighted cross-entropy loss function was used to reduce the rate of missed diagnosis. Results The proposed method was tested on data set constructed in this study. The precision, recall rate, accuracy and F1 value of the proposed method were 83%, 96%, 90% and 88% respectively, and it took 236 ms to complete COVID-19 recognition on medical computer. Conclusion The proposed method is superior to other algorithms in efficiency and accuracy, and it can better meet the needs of COVID-19 diagnosis.