ArticlePattern recognition letters2021
Two-Stage Deep Learning Framework for Discrimination between COVID-19 and Community-Acquired Pneumonia from Chest CT scans.
Article in Pattern recognition letters, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- A Systematic Review on Deep Structured Learning for COVID-19 Screening Using Chest CT from 2020 to 2022.Healthcare (Basel, Switzerland) · 2023Review
- Improve the performance of CT-based pneumonia classification via source data reweighting.Scientific reports · 2023Article
- Automatic COVID-19 and Common-Acquired Pneumonia Diagnosis Using Chest CT Scans.Bioengineering (Basel, Switzerland) · 2023Article
- Classification of COVID-19 from community-acquired pneumonia: Boosting the performance with capsule network and maximum intensity projection image of CT scans.Computers in biology and medicine · 2023Article
- Omnidirectional 2.5D representation for COVID-19 diagnosis using chest CTs.Journal of visual communication and image representation · 2023Article
- ACSN: Attention capsule sampling network for diagnosing COVID-19 based on chest CT scans.Computers in biology and medicine · 2023Article
- McS-Net: Multi-class Siamese network for severity of COVID-19 infection classification from lung CT scan slices.Applied soft computing · 2022Article
- The Caliber of Segmental and Subsegmental Vessels in COVID-19 Pneumonia Is Enlarged: A Distinctive Feature in Comparison with Other Forms of Inflammatory and Thromboembolic Diseases.Journal of personalized medicine · 2022Article
- Fully automatic pipeline of convolutional neural networks and capsule networks to distinguish COVID-19 from community-acquired pneumonia via CT images.Computers in biology and medicine · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
COVID-19 stay threatening the health infrastructure worldwide. Computed tomography (CT) was demonstrated as an informative tool for the recognition, quantification, and diagnosis of this kind of disease. It is urgent to design efficient deep learning (DL) approach to automatically localize and discriminate COVID-19 from other comparable pneumonia on lung CT scans. Thus, this study introduces a novel two-stage DL framework for discriminating COVID-19 from community-acquired pneumonia (CAP) depending on the detected infection region within CT slices. Firstly, a novel U-shaped network is presented to segment the lung area where the infection appears. Then, the concept of transfer learning is applied to the feature extraction network to empower the network capabilities in learning the disease patterns. After that, multi-scale information is captured and pooled via an attention mechanism for powerful classification performance. Thirdly, we propose an infection prediction module that use the infection location to guide the classification decision and hence provides interpretable classification decision. Finally, the proposed model was evaluated on public datasets and achieved great segmentation and classification performance outperforming the cutting-edge studies.
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Registered trials
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