论文标题
在小型数据集上使用深度学习从胸部X射线查找Covid-19
Finding Covid-19 from Chest X-rays using Deep Learning on a Small Dataset
论文作者
论文摘要
COVID-19的测试无法跟上需求。此外，假阴性率预计将高达30％，测试结果可能需要一些时间才能获得。 X射线机广泛可用，并提供图像以快速诊断。本文探讨了胸部X射线图像在诊断Covid-19疾病中的有用X射线图像。我们获得了122张胸部X射线，并获得了超过4,000多个病毒和细菌性肺炎的胸部X射线。在102例COVID-19病例和102例其他肺炎病例中，已预处理的深卷积神经网络在10倍的交叉验证中进行了调整。结果均为所有102 COVID-19病例，都正确分类，并且有8个假阳性导致AUC为0.997。在20个看不见的COVID-19病例的测试集中，所有其他肺炎示例中的4171例中有95％以上被正确分类。这项研究存在缺陷，最严重的是缺乏有关疾病过程中的疾病过程中的疾病和小数据集大小的信息。更多的COVID-19案例图像可以更好地回答胸部X射线对诊断Covid-19的有用问题的问题（因此，请发送它们）。
Testing for COVID-19 has been unable to keep up with the demand. Further, the false negative rate is projected to be as high as 30% and test results can take some time to obtain. X-ray machines are widely available and provide images for diagnosis quickly. This paper explores how useful chest X-ray images can be in diagnosing COVID-19 disease. We have obtained 122 chest X-rays of COVID-19 and over 4,000 chest X-rays of viral and bacterial pneumonia. A pretrained deep convolutional neural network has been tuned on 102 COVID-19 cases and 102 other pneumonia cases in a 10-fold cross validation. The results were all 102 COVID-19 cases were correctly classified and there were 8 false positives resulting in an AUC of 0.997. On a test set of 20 unseen COVID-19 cases all were correctly classified and more than 95% of 4171 other pneumonia examples were correctly classified. This study has flaws, most critically a lack of information about where in the disease process the COVID-19 cases were and the small data set size. More COVID-19 case images will enable a better answer to the question of how useful chest X-rays can be for diagnosing COVID-19 (so please send them).
