{"title":"COVID-19 Patients Detection in Chest X-ray Images via MCFF-Net","authors":"Wen Wang, Yutao Li, Xin Wang, Ji Li, Peng Zhang","doi":"10.1109/ICACI52617.2021.9435874","DOIUrl":null,"url":null,"abstract":"COVID-19 is a respiratory disease caused by severe acute respiratory syndrome coronavirus (SARS-CoV-2). This paper proposes a deep learning model to assist medical imaging physicians in diagnosing COVID-19 cases. We designed the Parallel Channel Attention Feature Fusion Module (PCAF), and brand new structure of convolutional neural network MCFF-Net was put forward. The experimental results show that the overall accuracy of MCFF-Net66-Conv1-GAP model is 96.79% for 3-class classification. Simultaneously, the precision, recall, specificity and the sensitivity for COVID-19 are both 100%. Compared with the latest state-of-art methods, the experimental results of our proposed method indicate its uniqueness.","PeriodicalId":382483,"journal":{"name":"2021 13th International Conference on Advanced Computational Intelligence (ICACI)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 13th International Conference on Advanced Computational Intelligence (ICACI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICACI52617.2021.9435874","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
COVID-19 is a respiratory disease caused by severe acute respiratory syndrome coronavirus (SARS-CoV-2). This paper proposes a deep learning model to assist medical imaging physicians in diagnosing COVID-19 cases. We designed the Parallel Channel Attention Feature Fusion Module (PCAF), and brand new structure of convolutional neural network MCFF-Net was put forward. The experimental results show that the overall accuracy of MCFF-Net66-Conv1-GAP model is 96.79% for 3-class classification. Simultaneously, the precision, recall, specificity and the sensitivity for COVID-19 are both 100%. Compared with the latest state-of-art methods, the experimental results of our proposed method indicate its uniqueness.