{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/covidctnet-an-open-source-deep-learning","title":"CovidCTNet: An Open-Source Deep Learning Approach to Identify Covid-19 Using CT Image","arxiv_id":"2005.03059","date":"2020-05-06","proceeding":null,"authors":["Tahereh Javaheri","Morteza Homayounfar","Zohreh Amoozgar","Reza Reiazi","Fatemeh Homayounieh","Engy Abbas","Azadeh Laali","Amir Reza Radmard","Mohammad Hadi Gharib","Seyed Ali Javad Mousavi","Omid Ghaemi","Rosa Babaei","Hadi Karimi Mobin","Mehdi Hosseinzadeh","Rana Jahanban-Esfahlan","Khaled Seidi","Mannudeep K. Kalra","Guanglan Zhang","L. T. Chitkushev","Benjamin Haibe-Kains","Reza Malekzadeh","Reza Rawassizadeh"],"abstract":"Coronavirus disease 2019 (Covid-19) is highly contagious with limited treatment options. Early and accurate diagnosis of Covid-19 is crucial in reducing the spread of the disease and its accompanied mortality. Currently, detection by reverse transcriptase polymerase chain reaction (RT-PCR) is the gold standard of outpatient and inpatient detection of Covid-19. RT-PCR is a rapid method, however, its accuracy in detection is only ~70-75%. Another approved strategy is computed tomography (CT) imaging. CT imaging has a much higher sensitivity of ~80-98%, but similar accuracy of 70%. To enhance the accuracy of CT imaging detection, we developed an open-source set of algorithms called CovidCTNet that successfully differentiates Covid-19 from community-acquired pneumonia (CAP) and other lung diseases. CovidCTNet increases the accuracy of CT imaging detection to 90% compared to radiologists (70%). The model is designed to work with heterogeneous and small sample sizes independent of the CT imaging hardware. In order to facilitate the detection of Covid-19 globally and assist radiologists and physicians in the screening process, we are releasing all algorithms and parametric details in an open-source format. Open-source sharing of our CovidCTNet enables developers to rapidly improve and optimize services, while preserving user privacy and data ownership.","url_abs":"https://arxiv.org/abs/2005.03059v3","url_pdf":"https://arxiv.org/pdf/2005.03059v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"covidctnet-an-open-source-deep-learning","repo_url":"https://github.com/mohofar/CovidCtNet","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"covid-19-detection","task_name":"COVID-19 Diagnosis"},{"task_slug":"covid-19-image-segmentation","task_name":"COVID-19 Image Segmentation"},{"task_slug":"computed-tomography-ct","task_name":"Computed Tomography (CT)"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/covid-19-diagnosis-on","task":"COVID-19 Diagnosis","dataset":"","model":"CovidCTNet","rank_in_archive_order":1,"of":2,"metrics":{"10 fold Cross validation":"90"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}