{"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/covid-cxnet-detecting-covid-19-in-frontal","title":"COVID-CXNet: Detecting COVID-19 in Frontal Chest X-ray Images using Deep Learning","arxiv_id":"2006.13807","date":"2020-06-16","proceeding":null,"authors":["Arman Haghanifar","Mahdiyar Molahasani Majdabadi","Younhee Choi","S. Deivalakshmi","Seokbum Ko"],"abstract":"One of the primary clinical observations for screening the infectious by the novel coronavirus is capturing a chest x-ray image. In most of the patients, a chest x-ray contains abnormalities, such as consolidation, which are the results of COVID-19 viral pneumonia. In this study, research is conducted on efficiently detecting imaging features of this type of pneumonia using deep convolutional neural networks in a large dataset. It is demonstrated that simple models, alongside the majority of pretrained networks in the literature, focus on irrelevant features for decision-making. In this paper, numerous chest x-ray images from various sources are collected, and the largest publicly accessible dataset is prepared. Finally, using the transfer learning paradigm, the well-known CheXNet model is utilized for developing COVID-CXNet. This powerful model is capable of detecting the novel coronavirus pneumonia based on relevant and meaningful features with precise localization. COVID-CXNet is a step towards a fully automated and robust COVID-19 detection system.","url_abs":"https://arxiv.org/abs/2006.13807v2","url_pdf":"https://arxiv.org/pdf/2006.13807v2.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":"covid-cxnet-detecting-covid-19-in-frontal","repo_url":"https://github.com/armiro/COVID-CXNet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"decision-making","task_name":"Decision Making"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"multi-class-classification","task_name":"Multi-class Classification"},{"task_slug":"pneumonia-detection","task_name":"Pneumonia Detection"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"chexnet","method_name":"CheXNet"},{"method_slug":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dense-block","method_name":"Dense Block"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"kaiming-initialization","method_name":"Kaiming Initialization"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"u-net","method_name":"U-Net"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/multi-class-classification-on-covid-19-cxr","task":"Multi-class Classification","dataset":"COVID-19 CXR Dataset","model":"COVID-CXNet","rank_in_archive_order":1,"of":1,"metrics":{"Accuracy (%)":"94.2"},"uses_additional_data":false},{"leaderboard":"/sota/pneumonia-detection-on-covid-19-cxr-dataset","task":"Pneumonia Detection","dataset":"COVID-19 CXR Dataset","model":"COVID-CXNet","rank_in_archive_order":1,"of":1,"metrics":{"F-Score":"0.85"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}