School of Biomedical Engineering, Guangdong Medical University, Dongguan, Guangdong, China.
Counties Manukau District Health Board, Auckland, 1640, New Zealand.
Sci Rep. 2021 Aug 9;11(1):16071. doi: 10.1038/s41598-021-95680-6.
To speed up the discovery of COVID-19 disease mechanisms by X-ray images, this research developed a new diagnosis platform using a deep convolutional neural network (DCNN) that is able to assist radiologists with diagnosis by distinguishing COVID-19 pneumonia from non-COVID-19 pneumonia in patients based on chest X-ray classification and analysis. Such a tool can save time in interpreting chest X-rays and increase the accuracy and thereby enhance our medical capacity for the detection and diagnosis of COVID-19. The explainable method is also used in the DCNN to select instances of the X-ray dataset images to explain the behavior of training-learning models to achieve higher prediction accuracy. The average accuracy of our method is above 96%, which can replace manual reading and has the potential to be applied to large-scale rapid screening of COVID-9 for widely use cases.
为了通过 X 射线图像加速 COVID-19 疾病机制的发现，本研究开发了一个新的诊断平台，使用深度卷积神经网络（DCNN），能够通过对胸部 X 射线分类和分析，根据 COVID-19 肺炎和非 COVID-19 肺炎来协助放射科医生进行诊断。这种工具可以节省解释胸部 X 射线的时间，并提高准确性，从而增强我们对 COVID-19 的检测和诊断能力。该可解释方法还用于 DCNN 中，以选择 X 射线数据集图像的实例来解释训练学习模型的行为，以实现更高的预测准确性。我们的方法的平均准确率在 96%以上，可以替代人工阅读，并有可能应用于 COVID-9 的大规模快速筛查，以便广泛使用。
