[论文解读] Detection of Coronavirus (COVID-19) Associated Pneumonia based on Generative Adversarial Networks and a Fine-Tuned Deep Transfer Learning Model using Chest X-ray Dataset

Nour Eldeen M. Khalifa, Mohamed Hamed N. Taha+2 more|arXiv (Cornell University)|Apr 2, 2020

COVID-19 diagnosis using AI被引用 61

一句话总结

论文使用基于GAN的数据增强和微调的深度迁移学习模型，从胸部X光中检测COVID-19相关肺炎，在有限数据集上ResNet18达到99%的测试准确率。

ABSTRACT

The COVID-19 coronavirus is one of the devastating viruses according to the world health organization. This novel virus leads to pneumonia, which is an infection that inflames the lungs' air sacs of a human. One of the methods to detect those inflames is by using x-rays for the chest. In this paper, a pneumonia chest x-ray detection based on generative adversarial networks (GAN) with a fine-tuned deep transfer learning for a limited dataset will be presented. The use of GAN positively affects the proposed model robustness and made it immune to the overfitting problem and helps in generating more images from the dataset. The dataset used in this research consists of 5863 X-ray images with two categories: Normal and Pneumonia. This research uses only 10% of the dataset for training data and generates 90% of images using GAN to prove the efficiency of the proposed model. Through the paper, AlexNet, GoogLeNet, Squeeznet, and Resnet18 are selected as deep transfer learning models to detect the pneumonia from chest x-rays. Those models are selected based on their small number of layers on their architectures, which will reflect in reducing the complexity of the models and the consumed memory and time. Using a combination of GAN and deep transfer models proved it is efficiency according to testing accuracy measurement. The research concludes that the Resnet18 is the most appropriate deep transfer model according to testing accuracy measurement and achieved 99% with the other performance metrics such as precision, recall, and F1 score while using GAN as an image augmenter. Finally, a comparison result was carried out at the end of the research with related work which used the same dataset except that this research used only 10% of original dataset. The presented work achieved a superior result than the related work in terms of testing accuracy.

研究动机与目标

• 推动从胸部X光片检测COVID-19肺炎。
• 通过使用基于GAN的图像生成来扩充数据集，以解决数据有限的问题。
• 评估用于肺炎检测的轻量级迁移学习模型。
• 在AlexNet、GoogLeNet、SqueezeNet和ResNet18中识别性能最好的模型。
• 通过GAN数据增强展示对过拟合的鲁棒性。

提出的方法

• 使用生成对抗网络（GAN）生成额外的胸部X光图像来扩充训练数据。
• 对5863张标注为Normal或Pneumonia的X光图像数据集应用微调的深度迁移学习。
• 评估四种轻量级架构（AlexNet、GoogLeNet、SqueezeNet、ResNet18）在肺炎检测中的表现。
• 用数据集的10%进行训练，并通过GAN生成90%的合成图像以测试鲁棒性和性能。
• 使用准确率及其他指标（精确率、召回率、F1）衡量性能。
• 基于测试准确率确定表现最佳的模型。

实验结果

研究问题

• RQ1 GAN生成的图像能否有效扩充用于肺炎检测的小型胸部X光数据集？
• RQ2 在此增强数据集上，哪个轻量级深度迁移学习模型能获得最高的COVID-19肺炎检测测试准确率？
• RQ3 GAN增强是否在评估的模型中减少过拟合并提升性能？
• RQ4 在GAN增强下，Normal与Pneumonia两类的对比性能指标（精确率、召回率、F1）是多少？

主要发现

• 采用原始数据集较小的GAN增强可以实现较高的测试准确率。
• 在这些模型中，ResNet18取得最佳性能。
• 该方法在增强数据集上实现了99%的测试准确率，以及相应的精确率、召回率与F1分数。
• 研究报告在同一数据集、10%训练数据条件下优于相关工作。
• 在本情境下，基于GAN的增强提高了模型对过拟合的鲁棒性。