{"title":"MCFF-Net在胸部x线图像中检测COVID-19患者","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":"22-23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"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\":\"22-23 1\",\"pages\":\"0\"},\"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}","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}
COVID-19 Patients Detection in Chest X-ray Images via MCFF-Net
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.