Automatic detection of multiple types of pneumonia: Open dataset and a multi-scale attention network. (March 2022)

• Record Type: Journal Article
• Authors: Wong, Pak Kin; Yan, Tao; Wang, Huaqiao; Chan, In Neng; Wang, Jiangtao; Li, Yang; Ren, Hao; Wong, Chi Hong
• Is Part Of: Biomedical signal processing and control. Volume 73(2022)
• Publication Date: 2022-03
• Subjects: COVID-19 -- Pneumonia identification -- Multi-scale convolution neural network -- Attention mechanism -- Chest computed tomography; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.103415
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: A chest CT dataset contains multiple types of pneumonia is available for the public. Established and analyzed a multi-scale attention network for pneumonia detection. Spatial attention block can enhance the performance of convolutional neural networks. Multiclass focal loss can effectively handle the data imbalance issue. Abstract: 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 .