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

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 c

SUBMITTER: Wong PK 

PROVIDER: S-EPMC8660060 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature