COVID19 Diagnosis Using Chest X-rays and Transfer Learning.
COVID19 Diagnosis Using Chest X-rays and Transfer Learning.
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DOI：
10.1101/2022.10.09.22280877
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发表时间：
2022-10-12
期刊：
影响因子：
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通讯作者：
Huang X
中科院分区：
文献类型：
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作者：
Stubblefield J;Causey J;Dale D;Qualls J;Bellis E;Fowler J;Walker K;Huang X
自2019年12月以来，一种名为SARS-CoV-2的新型冠状病毒引发的呼吸道疾病大流行席卷了全球。这呼吁包括医学成像在内的研究界提供有效的工具来抗击这种病毒。病毒患者的生物医学成像研究已经非常活跃，人们正在创建机器学习模型，利用CT扫描和胸部X光诊断患者的SARS-CoV-2感染。我们的目标是在这项研究的基础上再接再厉。在这里，我们使用迁移学习方法来开发能够从胸部X光诊断柯萨奇病毒19的模型。在这项工作中，我们汇编了来自胸部X光14的112120张阴性图像和来自公共存储库的2725张阳性图像的数据集。我们测试了多个模型，包括Logistic回归、随机森林和XGBoost，使用和不使用主成分分析，使用五次交叉验证来评估召回率、精确度和F1得分。这些模型与用于评估胸部X光的预先训练的深度学习模型COVID-Net进行了比较。我们最好的模型是XGBoost，主成分的召回率、查准率和F1得分分别为0.692、0.960和0.804。这一模型大大超过了得分为0.987、0.025、0.048的COVID-Net。该模型具有较高的精确度和合理的灵敏度，最适合作为COVID19的“合规”检测。尽管它在敏感性上优于一些化学分析，但该模型应该研究那些在用于筛查之前通常不接受胸部X光检查的患者。
A pandemic of respiratory illnesses from a novel coronavirus known as Sars-CoV-2 has swept across the globe since December of 2019. This is calling upon the research community including medical imaging to provide effective tools for use in combating this virus. Research in biomedical imaging of viral patients is already very active with machine learning models being created for diagnosing Sars-CoV-2 infections in patients using CT scans and chest x-rays. We aim to build upon this research. Here we used a transfer-learning approach to develop models capable of diagnosing COVID19 from chest x-ray. For this work we compiled a dataset of 112120 negative images from the Chest X-Ray 14 and 2725 positive images from public repositories. We tested multiple models, including logistic regression and random forest and XGBoost with and without principal components analysis, using five-fold cross-validation to evaluate recall, precision, and f1-score. These models were compared to a pre-trained deep-learning model for evaluating chest x-rays called COVID-Net. Our best model was XGBoost with principal components with a recall, precision, and f1-score of 0.692, 0.960, 0.804 respectively. This model greatly outperformed COVID-Net which scored 0.987, 0.025, 0.048. This model, with its high precision and reasonable sensitivity, would be most useful as “rule-in” test for COVID19. Though it outperforms some chemical assays in sensitivity, this model should be studied in patients who would not ordinarily receive a chest x-ray before being used for screening.