Theodora Sanida, Argyrios Sideris, Antonios Chatzisavvas, Michael F. Dossis, M. Dasygenis
{"title":"Radiography Images with Transfer Learning on Embedded System","authors":"Theodora Sanida, Argyrios Sideris, Antonios Chatzisavvas, Michael F. Dossis, M. Dasygenis","doi":"10.1109/SEEDA-CECNSM57760.2022.9932978","DOIUrl":null,"url":null,"abstract":"A serious public health concern is Novel Coronavirus Disease (COVID-19), which spread quickly over the globe at the end of 2019. This coronavirus is still able to propagate rapidly even after two years. Chest X-rays are crucial for diagnosing infected individuals in the worldwide battle against this illness. Therefore, various COVID-19 quick classification technologies can provide excellent classification accuracy to help medical professionals make the best choices. Here, we propose a trustworthy, compact network that, with the aid of encouraging classification results, can correctly identify COVID-19 from chest X-rays. The experimental findings demonstrated that, in a low-power embedded system, the modified architecture of the proposed model produced excellent performance metrics for four classes. The suggested classification architecture had an overall accuracy speed of 97.67% and an f1-score of 97.64%. This classification model is better than the other classification models used to classify patients with COVID-19 infection.","PeriodicalId":68279,"journal":{"name":"计算机工程与设计","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"计算机工程与设计","FirstCategoryId":"1093","ListUrlMain":"https://doi.org/10.1109/SEEDA-CECNSM57760.2022.9932978","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
A serious public health concern is Novel Coronavirus Disease (COVID-19), which spread quickly over the globe at the end of 2019. This coronavirus is still able to propagate rapidly even after two years. Chest X-rays are crucial for diagnosing infected individuals in the worldwide battle against this illness. Therefore, various COVID-19 quick classification technologies can provide excellent classification accuracy to help medical professionals make the best choices. Here, we propose a trustworthy, compact network that, with the aid of encouraging classification results, can correctly identify COVID-19 from chest X-rays. The experimental findings demonstrated that, in a low-power embedded system, the modified architecture of the proposed model produced excellent performance metrics for four classes. The suggested classification architecture had an overall accuracy speed of 97.67% and an f1-score of 97.64%. This classification model is better than the other classification models used to classify patients with COVID-19 infection.