论文标题
深层卷积神经网络，用于使用胸部X射线检测的COVID-19检测
A Deep Convolutional Neural Network for COVID-19 Detection Using Chest X-Rays
论文作者
论文摘要
目的：我们根据密集的卷积网络提出图像分类器，并根据三个标签进行转移学习以对胸部X射线图像进行分类：Covid-19，肺炎和正常。 方法：我们使用NIH CHESTX-RAY14数据集作为中间步骤，微调了在ImageNet上预处理并应用两次传输学习方法的神经网络。我们还提出了一种称为输出神经元保持的新颖性，它改变了两次转移学习技术。为了阐明模型的作案操作，我们使用了层的相关性传播（LRP）来生成热图。 结果：我们能够在测试数据集中达到100％的测试精度。两次转移学习和输出神经元保持表现出令人鼓舞的结果，主要是在训练过程开始。尽管LRP透露X射线上的单词可以影响网络的预测，但我们发现这对准确性的影响很小。 结论：尽管仍然需要临床研究和较大的数据集来进一步确保良好的概括，但我们实现的最新表现表明，借助人工智能，胸部X射线可以成为一种廉价且准确的辅助方法，可用于COVID-19诊断。 LRP产生的热图提高了深神经网络的解释性，并指示了未来诊断研究的分析路径。两次通过输出神经元进行两次转移学习，以保持改进的性能。
Purpose: We present image classifiers based on Dense Convolutional Networks and transfer learning to classify chest X-ray images according to three labels: COVID-19, pneumonia and normal. Methods: We fine-tuned neural networks pretrained on ImageNet and applied a twice transfer learning approach, using NIH ChestX-ray14 dataset as an intermediate step. We also suggested a novelty called output neuron keeping, which changes the twice transfer learning technique. In order to clarify the modus operandi of the models, we used Layer-wise Relevance Propagation (LRP) to generate heatmaps. Results: We were able to reach test accuracy of 100% on our test dataset. Twice transfer learning and output neuron keeping showed promising results improving performances, mainly in the beginning of the training process. Although LRP revealed that words on the X-rays can influence the networks' predictions, we discovered this had only a very small effect on accuracy. Conclusion: Although clinical studies and larger datasets are still needed to further ensure good generalization, the state-of-the-art performances we achieved show that, with the help of artificial intelligence, chest X-rays can become a cheap and accurate auxiliary method for COVID-19 diagnosis. Heatmaps generated by LRP improve the interpretability of the deep neural networks and indicate an analytical path for future research on diagnosis. Twice transfer learning with output neuron keeping improved performances.
