COVID-CT19-挑战：COVID-CT19-挑战

# COVID-CT19-Challenge > This is an example of classifying each CT image into positive COVID-19 (the image has clinical findings of COVID-19) or negative COVID-19 ( the image does not have clinical findings of COVID-19).  ## Prerequisities The following dependencies are needed: - numpy >= 1.11.1 - opencv-python >=3.3.0 - tensorflow-gpu ==1.8.0 - pandas >=0.20.1 - scikit-learn >= 0.17.1 ## Downlod DataSet * you can download dataset from here [link](https://github.com/UCSD-AI4H/COVID-CT) * The dataset details are described in this preprint: [COVID-CT-Dataset: A CT Scan Dataset about COVID-19](https://arxiv.org/pdf/2003.13865.pdf) * If you find this dataset and code useful, please cite: @article{zhao2020COVID-CT-Dataset, title={COVID-CT-Dataset: a CT scan dataset about COVID-19}, author={Zhao, Jinyu and Zhang, Yichen and He, Xuehai and Xie, Pengtao}, journal={arXiv preprint arXiv:2003.13865}, year={2020} } ## How to Use **1、Preprocess** * split source data into training data,validation data,test data. * augmentation training data * write all data into csv file:you can run the data2dprepare.py one step by one. **2、Positive and Negative COVID-19 Classify** * the RestNet model * training process:run the ResNet2d_train.py * predict process:run the ResNet2d_predict.py ## Result * the train loss and accuracy   * test data result:F1 score is 0.77,AUC area is 0.833,accuracy is 0.77. ## Contact * https://github.com/junqiangchen * email: 1207173174@qq.com * Contact: junqiangChen * WeChat Number: 1207173174 * WeChat Public number: 最新医学影像技术