论文标题
COVID-19使用深度学习两阶段方法的分类
COVID-19 Classification Using Deep Learning Two-Stage Approach
论文作者
论文摘要
在本文中，已经使用了基于深度学习的方法，即对验证的卷积神经网络（VGG16和VGG19）进行微调，以及对开发的CNN模型的端到端培训，以将X射线图像分类为四个不同类别，包括四个不同的类别，包括COVID-19，正常，透明，透明度和pneumonia病例。从Kaggle检索了一个包含20,000多个X射线扫描的数据集并在本实验中使用。实施了一种两阶段的分类方法，以将其与单发分类方法进行比较。我们的假设是，一个两阶段的模型将能够比单光模型获得更好的性能。我们的结果表明，由于VGG16使用超过5倍训练的方法达到了95％的精度。未来的工作将集中于更强大的两阶段分类模型Covid-TSC。主要的改进将允许数据从阶段1的输出流到阶段2的输入，在阶段-1和2阶段模型中是在COVID-19数据集中微调的VGG16模型。
In this paper, deep-learning-based approaches namely fine-tuning of pretrained convolutional neural networks (VGG16 and VGG19), and end-to-end training of a developed CNN model, have been used in order to classify X-Ray images into four different classes that include COVID-19, normal, opacity and pneumonia cases. A dataset containing more than 20,000 X-ray scans was retrieved from Kaggle and used in this experiment. A two-stage classification approach was implemented to be compared to the one-shot classification approach. Our hypothesis was that a two-stage model will be able to achieve better performance than a one-shot model. Our results show otherwise as VGG16 achieved 95% accuracy using one-shot approach over 5-fold of training. Future work will focus on a more robust implementation of the two-stage classification model Covid-TSC. The main improvement will be allowing data to flow from the output of stage-1 to the input of stage-2, where stage-1 and stage-2 models are VGG16 models fine-tuned on the Covid-19 dataset.
