Nikolaou Vasilis, Massaro Sebastiano, Fakhimi Masoud, Stergioulas Lampros, Garn Wolfgang
Surrey Business School, University of Surrey, Alexander Fleming Rd, Guildford, GU2 7XH UK.
The Organizational Neuroscience Laboratory, London, WC1N 3AX UK.
Health Inf Sci Syst. 2021 Oct 12;9(1):36. doi: 10.1007/s13755-021-00166-4. eCollection 2021 Dec.
Chest x-rays are a fast and inexpensive test that may potentially diagnose COVID-19, the disease caused by the novel coronavirus. However, chest imaging is not a first-line test for COVID-19 due to low diagnostic accuracy and confounding with other viral pneumonias. Recent research using deep learning may help overcome this issue as convolutional neural networks (CNNs) have demonstrated high accuracy of COVID-19 diagnosis at an early stage.
We used the COVID-19 Radiography database [36], which contains x-ray images of COVID-19, other viral pneumonia, and normal lungs. We developed a CNN in which we added a dense layer on top of a pre-trained baseline CNN (EfficientNetB0), and we trained, validated, and tested the model on 15,153 X-ray images. We used data augmentation to avoid overfitting and address class imbalance; we used fine-tuning to improve the model's performance. From the external test dataset, we calculated the model's accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1-score.
Our model differentiated COVID-19 from normal lungs with 95% accuracy, 90% sensitivity, and 97% specificity; it differentiated COVID-19 from other viral pneumonia and normal lungs with 93% accuracy, 94% sensitivity, and 95% specificity.
Our parsimonious CNN shows that it is possible to differentiate COVID-19 from other viral pneumonia and normal lungs on x-ray images with high accuracy. Our method may assist clinicians with making more accurate diagnostic decisions and support chest X-rays as a valuable screening tool for the early, rapid diagnosis of COVID-19.
The online version contains supplementary material available at 10.1007/s13755-021-00166-4.
胸部X光检查是一种快速且廉价的检查方法，有可能诊断出由新型冠状病毒引起的COVID-19疾病。然而，由于诊断准确性较低且易与其他病毒性肺炎混淆，胸部成像并非COVID-19的一线检查方法。最近使用深度学习的研究可能有助于克服这一问题，因为卷积神经网络（CNN）已在早期COVID-19诊断中显示出高准确性。
我们使用了COVID-19放射影像数据库[36]，其中包含COVID-19、其他病毒性肺炎和正常肺部的X光图像。我们开发了一个CNN，在预训练的基线CNN（EfficientNetB0）之上添加了一个密集层，并在15153张X光图像上对该模型进行了训练、验证和测试。我们使用数据增强来避免过拟合并解决类别不平衡问题；我们使用微调来提高模型的性能。从外部测试数据集中，我们计算了模型的准确性、敏感性、特异性、阳性预测值、阴性预测值和F1分数。
我们的模型区分COVID-19与正常肺部的准确率为95%，敏感性为90%，特异性为97%；它区分COVID-19与其他病毒性肺炎和正常肺部的准确率为93%，敏感性为94%，特异性为95%。
我们简约的CNN表明，在X光图像上以高准确率区分COVID-19与其他病毒性肺炎和正常肺部是可行的。我们的方法可能有助于临床医生做出更准确的诊断决策，并支持胸部X光作为COVID-19早期快速诊断的有价值筛查工具。
在线版本包含可在10.1007/s13755-021-00166-4获取的补充材料。
