Wong Pak Kin, Yan Tao, Wang Huaqiao, Chan In Neng, Wang Jiangtao, Li Yang, Ren Hao, Wong Chi Hong
Department of Electromechanical Engineering, University of Macau, Taipa 999078, Macau.
School of Mechanical Engineering, Hubei University of Arts and Science, Xiangyang 441053, China.
Biomed Signal Process Control. 2022 Mar;73:103415. doi: 10.1016/j.bspc.2021.103415. Epub 2021 Dec 9.
The quick and precise identification of COVID-19 pneumonia, non-COVID-19 viral pneumonia, bacterial pneumonia, mycoplasma pneumonia, and normal lung on chest CT images play a crucial role in timely quarantine and medical treatment. However, manual identification is subject to potential misinterpretations and time-consumption issues owing the visual similarities of pneumonia lesions. In this study, we propose a novel multi-scale attention network (MSANet) based on a bag of advanced deep learning techniques for the automatic classification of COVID-19 and multiple types of pneumonia. The proposed method can automatically pay attention to discriminative information and multi-scale features of pneumonia lesions for better classification. The experimental results show that the proposed MSANet can achieve an overall precision of 97.31%, recall of 96.18%, F1-score of 96.71%, accuracy of 97.46%, and macro-average area under the receiver operating characteristic curve (AUC) of 0.9981 to distinguish between multiple classes of pneumonia. These promising results indicate that the proposed method can significantly assist physicians and radiologists in medical diagnosis. The dataset is publicly available at https://doi.org/10.17632/rf8x3wp6ss.1.
在胸部CT图像上快速准确地识别新型冠状病毒肺炎（COVID-19肺炎）、非COVID-19病毒性肺炎、细菌性肺炎、支原体肺炎和正常肺，对于及时隔离和治疗至关重要。然而，由于肺炎病变在视觉上存在相似性，人工识别可能会出现误判且耗时。在本研究中，我们基于一系列先进的深度学习技术，提出了一种新型的多尺度注意力网络（MSANet），用于对COVID-19和多种类型的肺炎进行自动分类。所提出的方法能够自动关注肺炎病变的判别信息和多尺度特征，以实现更好的分类。实验结果表明，所提出的MSANet在区分多类肺炎时，总体精度达到97.31%，召回率为96.18%，F1分数为96.71%，准确率为97.46%，接收器操作特征曲线（AUC）的宏平均面积为0.9981。这些令人鼓舞的结果表明，所提出的方法能够显著辅助医生和放射科医生进行医学诊断。该数据集可在https://doi.org/10.17632/rf8x3wp6ss.1上公开获取。
