Per-Covid-19: A Benchmark Database For Covid-19 Percentage Prediction From CT-scans

Per-Covid-19: A Benchmark Database For Covid-19 Percentage Prediction From CT-scans | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Per-Covid-19: A Benchmark Database For Covid-19 Percentage Prediction From CT-scans Fares Bougourzi, Cosimo Distante, Ouafi Abdelkrim, Fadi Dornaika, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-491375/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Covid-19 infection recognition is very important step in the fighting against the new pandemic Covid-19. In fact, many methods have been used to recognize the Covid-19 infection including Reverse transcription polymerase chain reaction (RT-PCR), X-ray scan and CT-scan. In addition to the recognition of the Covid-19 infection, CT-scans can provide more important information about the evolution of this disease and its severity. With the extensive number of Covid-19 infections, estimating the Covid-19 percentage can help the intensive care to free up the resuscitation beds for the critical cases and follow other protocol for less severity cases. In this paper, we propose Covid-19 percentage estimation database. Moreover, we evaluate the performance of three Covolutional Neural Network (CNN) architectures which are ResneXt-50, Densenet-161 and Inception-v3. For the three CNN architectures, we use two loss functions which are MSE and Dynamic Huber. In addition, two pretrained scenarios are investigated (ImageNet pretrained models and X-ray pretrained models). The evaluated approaches achieved promising results, where Inception-v3 with using Dynamic Huber loss function and X-ray pretrained model achieved the best performance. Biomedical Engineering Computational Mathematics Covid-19 infections Covolutional Neural Network RT-PCR Figures Figure 1 Figure 2 Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-491375","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":25621081,"identity":"111f1cff-afee-4013-8940-f5d4e56b12f3","order_by":0,"name":"Fares Bougourzi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYBAC9gYgwdgA4xrYgLiNB/Bp4TmAqiUNzCVFC8NhMIlfC/vZhw9/7rBJnO/eY/bxR8F5u7Xth4G21NhE49TCk25szHsmLXHjmTPGMyQMbidvO5MI1HIsLbcBhxZ7hjQ2aca2w4kbZ+QYMxgAtZgdAGphbDiMUwsP/zP2nz9hWhIMziWbnX9IQItEGhsDL1DLfAmglgMGB+zMbhCyReIZszTQL8YbeI4VMzYYJCeY3QDakoDHLzz8aYwfgSEmO7+9eTPjjz929mbn0x8++FBjg1MLHBgcgNCJYJUJhJSDgDzUUHtiFI+CUTAKRsHIAgD9+GLjBLoi8QAAAABJRU5ErkJggg==","orcid":"","institution":"National Research Council of Italy","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Fares","middleName":"","lastName":"Bougourzi","suffix":""},{"id":25621082,"identity":"5f7a7c2e-0236-4dd0-bfa4-ee9f0f8d1dab","order_by":1,"name":"Cosimo Distante","email":"","orcid":"","institution":"National Research Council of Italy","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Cosimo","middleName":"","lastName":"Distante","suffix":""},{"id":25621083,"identity":"12561bbf-a858-445c-95fc-460c2d5ab63f","order_by":2,"name":"Ouafi Abdelkrim","email":"","orcid":"","institution":"University of Biskra","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ouafi","middleName":"","lastName":"Abdelkrim","suffix":""},{"id":25621084,"identity":"d1d905e8-a889-410d-a8c6-e03c6d912144","order_by":3,"name":"Fadi Dornaika","email":"","orcid":"","institution":"IKERBASQUE, Basque Foundation for Science","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Fadi","middleName":"","lastName":"Dornaika","suffix":""},{"id":25621085,"identity":"376461ca-ccc4-4084-b0da-7507e37e2c56","order_by":4,"name":"Abdenour Hadid","email":"","orcid":"","institution":"Univ. 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