{"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/cvr-net-a-deep-convolutional-neural-network","title":"CVR-Net: A deep convolutional neural network for coronavirus recognition from chest radiography images","arxiv_id":"2007.11993","date":"2020-07-21","proceeding":null,"authors":["Md. Kamrul Hasan","Md. Ashraful Alam","Md. Toufick E Elahi","Shidhartho Roy","Sifat Redwan Wahid"],"abstract":"The novel Coronavirus Disease 2019 (COVID-19) is a global pandemic disease spreading rapidly around the world. A robust and automatic early recognition of COVID-19, via auxiliary computer-aided diagnostic tools, is essential for disease cure and control. The chest radiography images, such as Computed Tomography (CT) and X-ray, and deep Convolutional Neural Networks (CNNs), can be a significant and useful material for designing such tools. However, designing such an automated tool is challenging as a massive number of manually annotated datasets are not publicly available yet, which is the core requirement of supervised learning systems. In this article, we propose a robust CNN-based network, called CVR-Net (Coronavirus Recognition Network), for the automatic recognition of the coronavirus from CT or X-ray images. The proposed end-to-end CVR-Net is a multi-scale-multi-encoder ensemble model, where we have aggregated the outputs from two different encoders and their different scales to obtain the final prediction probability. We train and test the proposed CVR-Net on three different datasets, where the images have collected from different open-source repositories. We compare our proposed CVR-Net with state-of-the-art methods, which are trained and tested on the same datasets. We split three datasets into five different tasks, where each task has a different number of classes, to evaluate the multi-tasking CVR-Net. Our model achieves an overall F1-score & accuracy of 0.997 & 0.998; 0.963 & 0.964; 0.816 & 0.820; 0.961 & 0.961; and 0.780 & 0.780, respectively, for task-1 to task-5. As the CVR-Net provides promising results on the small datasets, it can be an auspicious computer-aided diagnostic tool for the diagnosis of coronavirus to assist the clinical practitioners and radiologists. Our source codes and model are publicly available at https://github.com/kamruleee51/CVR-Net.","url_abs":"https://arxiv.org/abs/2007.11993v1","url_pdf":"https://arxiv.org/pdf/2007.11993v1.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":"cvr-net-a-deep-convolutional-neural-network","repo_url":"https://github.com/kamruleee51/CVR-Net","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"computed-tomography-ct","task_name":"Computed Tomography (CT)"},{"task_slug":"diagnostic","task_name":"Diagnostic"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}