论文标题
胸部X射线图像的多渠道转移学习用于筛选Covid-19
Multi-Channel Transfer Learning of Chest X-ray Images for Screening of COVID-19
论文作者
论文摘要
2019年的小说冠状病毒（Covid-19）已在世界各地迅速传播，它正在影响整个社会。当前用于筛查Covid-19患者的金标准测试是聚合酶链反应测试。但是，COVID-19测试套件并不广泛且耗时。因此，作为替代方案，正在考虑快速筛选胸部X射线。由于胸部X射线中的Covid-19的呈现在特征上有所不同，并且需要专门阅读Covid-19胸部X射线，从而限制了其用于诊断的用途。为了解决放射科医生迅速阅读胸部X射线的挑战，我们提出了一个基于重新结构体系结构的多通道传输学习模型，以促进Covid-19胸部X射线的诊断。使用DataSet_A（1579正常和4429例），DataSet_B（4245个肺炎和1763个非pneumonia）和DataSet_C（分别为184 COVID-19和5824非covid19），使用DataSet_B（4245 pneumonia和1763 non-pneumonia），分别（或5824 nont-nor-connia），或分类（b）正常，或分类（b）正常（A）非肺炎，以及（c）covid-19或non-covid19。最后，使用Dataset_d（1579正常，4245肺炎和184 Covid-19）对这三个模型进行了结合和微调，以对正常，肺炎和Covid-19病例进行分类。我们的结果表明，集成模型比单个Resnet模型更准确，该模型还使用DataSet_d重新训练，因为它为每个类提取更相关的语义功能。我们的方法提供的精度为94％，召回100％。因此，我们的方法可能有可能帮助临床医生筛查患者的Covid-19，从而促进了立即进行分类和治疗以获得更好的结局。
The 2019 novel coronavirus (COVID-19) has spread rapidly all over the world and it is affecting the whole society. The current gold standard test for screening COVID-19 patients is the polymerase chain reaction test. However, the COVID-19 test kits are not widely available and time-consuming. Thus, as an alternative, chest X-rays are being considered for quick screening. Since the presentation of COVID-19 in chest X-rays is varied in features and specialization in reading COVID-19 chest X-rays are required thus limiting its use for diagnosis. To address this challenge of reading chest X-rays by radiologists quickly, we present a multi-channel transfer learning model based on ResNet architecture to facilitate the diagnosis of COVID-19 chest X-ray. Three ResNet-based models (Models a, b, and c) were retrained using Dataset_A (1579 normal and 4429 diseased), Dataset_B (4245 pneumonia and 1763 non-pneumonia), and Dataset_C (184 COVID-19 and 5824 Non-COVID19), respectively, to classify (a) normal or diseased, (b) pneumonia or non-pneumonia, and (c) COVID-19 or non-COVID19. Finally, these three models were ensembled and fine-tuned using Dataset_D (1579 normal, 4245 pneumonia, and 184 COVID-19) to classify normal, pneumonia, and COVID-19 cases. Our results show that the ensemble model is more accurate than the single ResNet model, which is also re-trained using Dataset_D as it extracts more relevant semantic features for each class. Our approach provides a precision of 94 % and a recall of 100%. Thus, our method could potentially help clinicians in screening patients for COVID-19, thus facilitating immediate triaging and treatment for better outcomes.
