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| Detection of Novel Coronavirus Pneumonia Based on CT Image with Convolutional Neural Network Processing |
| GUO Bao-su1,ZHUANG Ji-chao1,WU Feng-he1,CHE Xiao-shuang2,YUAN Lin-dong2,QI Jun2 |
1. College of Mechanical Engineering, Yanshan University, Qinhuangdao, Heibei 066004, China
2. Liaocheng Peoples Hospital, Liaocheng, Shandong 252000, China |
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Abstract To improve the ability to distinguish novel coronavirus pneumonia from common pneumonia and assist medical staff in chest CT examination of pneumonia patients, a detection method using convolution neural network and CT image based on artificial intelligence image analysis was proposed. First, a convolution neural network model was built, and the influence of model depth on detection results was evaluated to select the best network structure. Second, a tabu genetic algorithm was proposed to obtain the optimal hyper-parameter combination of the network model and to enhance the performance of the model. Finally, the best network model was employed to distinguish novel coronavirus pneumonia from common pneumonia. Experimental results show that the accuracy, MCC, and F1Score of the proposed detection algorithm are 93.89%, 93.32% and 91.40%, respectively, which has higher detection accuracy than other algorithms.
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Received: 17 April 2020
Published: 20 April 2021
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2021, Vol. 42 
