Abstract
PurposeThis study proposes a method to draw attention toward the specific radiological findings of coronavirus disease 2019 (COVID-19) in CT images, such as bilaterality of ground glass opacity (GGO) and/or consolidation, in order to improve the classification accuracy of input CT images.MethodsWe propose an induction mask that combines a similarity and a bilateral mask. A similarity mask guides attention to regions with similar appearances, and a bilateral mask induces attention to the opposite side of the lung to capture bilaterally distributed lesions. An induction mask for pleural effusion is also proposed in this study. ResNet18 with nonlocal blocks was trained by minimizing the loss function defined by the induction mask.ResultsThe four-class classification accuracy of the CT images of 1504 cases was 0.6443, where class 1 was the typical appearance of COVID-19 pneumonia, class 2 was the indeterminate appearance of COVID-19 pneumonia, class 3 was the atypical appearance of COVID-19 pneumonia, and class 4 was negative for pneumonia. The four classes were divided into two subgroups. The accuracy of COVID-19 and pneumonia classifications was evaluated, which were 0.8205 and 0.8604, respectively. The accuracy of the four-class and COVID-19 classifications improved when attention was paid to pleural effusion.ConclusionThe proposed attention induction method was effective for the classification of CT images of COVID-19 patients. Improvement of the classification accuracy of class 3 by focusing on features specific to the class remains a topic for future work.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: International journal of computer assisted radiology and surgery
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.