Automated triage of COVID-19 from various lung abnormalities using chest CT features
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2020
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| _version_ | 1866917820407742464 |
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| author | Amran, Dor Frid-Adar, Maayan Sagie, Nimrod Nassar, Jannette Kabakovitch, Asher Greenspan, Hayit |
| author_facet | Amran, Dor Frid-Adar, Maayan Sagie, Nimrod Nassar, Jannette Kabakovitch, Asher Greenspan, Hayit |
| contents | The outbreak of COVID-19 has lead to a global effort to decelerate the pandemic spread. For this purpose chest computed-tomography (CT) based screening and diagnosis of COVID-19 suspected patients is utilized, either as a support or replacement to reverse transcription-polymerase chain reaction (RT-PCR) test. In this paper, we propose a fully automated AI based system that takes as input chest CT scans and triages COVID-19 cases. More specifically, we produce multiple descriptive features, including lung and infections statistics, texture, shape and location, to train a machine learning based classifier that distinguishes between COVID-19 and other lung abnormalities (including community acquired pneumonia). We evaluated our system on a dataset of 2191 CT cases and demonstrated a robust solution with 90.8% sensitivity at 85.4% specificity with 94.0% ROC-AUC. In addition, we present an elaborated feature analysis and ablation study to explore the importance of each feature. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2010_12967 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | Automated triage of COVID-19 from various lung abnormalities using chest CT features Amran, Dor Frid-Adar, Maayan Sagie, Nimrod Nassar, Jannette Kabakovitch, Asher Greenspan, Hayit Image and Video Processing Computer Vision and Pattern Recognition The outbreak of COVID-19 has lead to a global effort to decelerate the pandemic spread. For this purpose chest computed-tomography (CT) based screening and diagnosis of COVID-19 suspected patients is utilized, either as a support or replacement to reverse transcription-polymerase chain reaction (RT-PCR) test. In this paper, we propose a fully automated AI based system that takes as input chest CT scans and triages COVID-19 cases. More specifically, we produce multiple descriptive features, including lung and infections statistics, texture, shape and location, to train a machine learning based classifier that distinguishes between COVID-19 and other lung abnormalities (including community acquired pneumonia). We evaluated our system on a dataset of 2191 CT cases and demonstrated a robust solution with 90.8% sensitivity at 85.4% specificity with 94.0% ROC-AUC. In addition, we present an elaborated feature analysis and ablation study to explore the importance of each feature. |
| title | Automated triage of COVID-19 from various lung abnormalities using chest CT features |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2010.12967 |