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Robust framework for COVID-19 identication from a multicenter dataset of chest CT scans

Author(s): Khademi S; Heidarian S; Afshar P; Enshaei N; Naderkhani F; Rafiee MJ; Oikonomou A; Shafiee A; Babaki Fard F; Plataniotis KN; Mohammadi A;

The main objective of this study is to develop a robust deep learning-based framework to distinguish COVID-19, Community-Acquired Pneumonia (CAP), and Normal cases based on volumetric chest CT scans, which are acquired in different imaging centers using different scanners and technical settings. ...

Article GUID: 36862633


Human-level COVID-19 diagnosis from low-dose CT scans using a two-stage time-distributed capsule network

Author(s): Afshar P; Rafiee MJ; Naderkhani F; Heidarian S; Enshaei N; Oikonomou A; Babaki Fard F; Anconina R; Farahani K; Plataniotis KN; Mohammadi A;

Reverse transcription-polymerase chain reaction is currently the gold standard in COVID-19 diagnosis. It can, however, take days to provide the diagnosis, and false negative rate is relatively high. Imaging, in particular chest computed tomography (CT), can assist with diagnosis and assessment of ...

Article GUID: 35318368


COVID-FACT: A Fully-Automated Capsule Network-Based Framework for Identification of COVID-19 Cases from Chest CT Scans

Author(s): Heidarian S; Afshar P; Enshaei N; Naderkhani F; Rafiee MJ; Babaki Fard F; Samimi K; Atashzar SF; Oikonomou A; Plataniotis KN; Mohammadi A;

The newly discovered Coronavirus Disease 2019 (COVID-19) has been globally spreading and causing hundreds of thousands of deaths around the world as of its first emergence in late 2019. The rapid outbreak of this disease has overwhelmed health care infrastructures and arises the need to allocate ...

Article GUID: 34113843


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