{"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/web-diagnosis-for-covid-19-and-pneumonia","title":"Web Diagnosis for COVID-19 and Pneumonia Based on Computed Tomography Scans and X-rays","arxiv_id":null,"date":"2024-01-06","proceeding":"Universal Access in Human-Computer Interaction 2024 1","authors":["Carlos Antunes","João Rodrigues","António Cunha"],"abstract":"Pneumonia and COVID-19 are respiratory illnesses, the last caused by the severe acute respiratory syndrome virus, coronavirus 2 (SARS-CoV-2). Traditional detection processes can be slow, prone to errors, and laborious, leading to potential human mistakes and a limited ability to keep up with the speed of pathogen development. A web diagnosis application to aid the physician in the diagnosis process is presented, based on a modified deep neural network (AlexNet) to detect COVID-19 on X-rays and computed tomography (CT) scans as well as to detect pneumonia on X-rays. The system reached accuracy results well above 90% in seven well-known and documented datasets regarding the detection of COVID-19 and Pneumonia on X-rays and COVID-19 in CT scans.","url_abs":"https://link.springer.com/chapter/10.1007/978-3-031-60884-1_14","url_pdf":"https://link.springer.com/chapter/10.1007/978-3-031-60884-1_14","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":"web-diagnosis-for-covid-19-and-pneumonia","repo_url":"https://github.com/kucaantunes/AI-Health","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null},{"paper_slug":"web-diagnosis-for-covid-19-and-pneumonia","repo_url":"https://github.com/kucaantunes/Automatic_Detection_of_Covid-19_on_CT_Scans","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"computed-tomography-ct","task_name":"Computed Tomography (CT)"}],"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":"cspresnext","method_name":"CSPResNeXt"},{"method_slug":"cspresnext-block","method_name":"CSPResNeXt Block"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"grouped-convolution","method_name":"Grouped Convolution"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"resnext-block","method_name":"ResNeXt Block"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"speed","method_name":"SPEED"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}