Journal
IEEE SENSORS JOURNAL
Volume 22, Issue 18, Pages 17431-17438Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSEN.2021.3062442
Keywords
Attention; covid-19; VGG; convolutional neural network; diagnosis; convolutional block attention module
Funding
- Royal Society International Exchanges Cost Share Award, U.K. [RP202G0230]
- Medical Research Council Confidence in Concept Award, U.K. [MC_PC_17171]
- Hope Foundation for Cancer Research, U.K. [RM60G0680]
- Fundamental Research Funds for the Central Universities [CDLS-2020-03]
- Key Laboratory of Child Development and Learning Science (Southeast University), Ministry of Education
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To detect COVID-19 patients more accurately, a 12-layer attention-based VGG-style network called AVNC was proposed, using a chest CT dataset and incorporating attention module and data augmentation method, achieving high sensitivity, precision, and F1 scores.
(Aim) To detect COVID-19 patients more accurately and more precisely, we proposed a novel artificial intelligence model. (Methods) We used previously proposed chest CT dataset containing four categories: COVID-19, community-acquired pneumonia, secondary pulmonary tuberculosis, and healthy subjects. First, we proposed a novel VGG-style base network (VSBN) as backbone network. Second, convolutional block attention module (CBAM) was introduced as attention module into our VSBN. Third, an improved multiple-way data augmentation method was used to resist over-fitting of our Al model. In all, our model was dubbed as a 12-layer attention-based VGG-style network for COVID-19 (AVNC) (Results) This proposed AVNC achieved the sensitivity/precision/F1 per class all above 95%. Particularly, AVNC yielded a micro-averaged F1 score of 96.87%, which is higher than 11 state-of-the-art approaches. (Conclusion) This proposed AVNC is effective in recognizing COVID-19 diseases.
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