Wang Shui-Hua, Fernandes Steven Lawrence, Zhu Ziquan, Zhang Yu-Dong
School of Mathematics and Actuarial ScienceUniversity of Leicester Leicester LE1 7RH U.K.
Department of Computer ScienceDesign & JournalismCreighton University Omaha NE 68178 USA.
IEEE Sens J. 2021 Feb 26;22(18):17431-17438. doi: 10.1109/JSEN.2021.3062442. eCollection 2022 Sep.
(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 overfitting of our AI 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.
（目的）为了更准确、更精确地检测新冠肺炎患者，我们提出了一种新型人工智能模型。（方法）我们使用了先前提出的包含四类的胸部CT数据集：新冠肺炎、社区获得性肺炎、继发性肺结核和健康受试者。首先，我们提出了一种新型的VGG风格基础网络（VSBN）作为骨干网络。其次，将卷积块注意力模块（CBAM）作为注意力模块引入到我们的VSBN中。第三，使用一种改进的多路数据增强方法来抵抗我们的人工智能模型的过拟合。总之，我们的模型被命名为用于新冠肺炎的基于注意力的12层VGG风格网络（AVNC）。（结果）所提出的AVNC实现了每类的灵敏度/精度/F1均高于95%。特别是，AVNC产生了96.87%的微平均F1分数，高于11种先进方法。（结论）所提出的AVNC在识别新冠肺炎疾病方面是有效的。
