Acta Metrologica Sinica  2023, Vol. 44 Issue (8): 1303-1309    DOI: 10.3969/j.issn.1000-1158.2023.08.23
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ConvOS:An Interpretable Diagnostic Model for COVID-19 X-ray Imaging
JIAN Xian-zhong,YOU Guo-da,ZHANG Tao,ZHANG Zhen-wen
School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200090, China
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Abstract  In order to improve the diagnostic accuracy of deep learning model in X-ray diagnosis of COVID-19 and improve the models interpretability, a novel visual diagnosis (ConvOS) model was designed by combining bilateral perturbation comprehensive Gradient significance graph optimization (IGOS++) algorithm and improved ConvNeXt network model. An efficient channel attention (ECA) module was introduced into the residual block of ConvNeXt network model, and IGOS++ was used to analyze the output characteristics of the improved ConvNeXt network model. The optimal insertion loss hyperparameters were obtained, and high-precision saliency maps were generated to improve the performance of visual interpretation and diagnosis of COVID-19 X-ray images. The experimental results on COVIDx dataset show that the accuracy, recall, accuracy and F1-score of ConvOS model reach 93.7%, 92.6%, 96.2% and 94.4%, respectively. Compared with other diagnostic models, the classification performance index is better, the region of interest (ROI) of significance graph is more accurate, and the diagnostic confidence of the model is higher.
Key wordsmetrology      COVID-19      X-ray radiography      ConvOS model      IGOS++ algorithm      ConvNeXt network      interpretability      deep learning model     
Received: 21 July 2022      Published: 22 August 2023
PACS:  TB99  
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JIAN Xian-zhong
YOU Guo-da
ZHANG Tao
ZHANG Zhen-wen
Cite this article:   
JIAN Xian-zhong,YOU Guo-da,ZHANG Tao, et al. ConvOS:An Interpretable Diagnostic Model for COVID-19 X-ray Imaging[J]. Acta Metrologica Sinica, 2023, 44(8): 1303-1309.
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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2023.08.23     OR     http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2023/V44/I8/1303
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