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

• The dataset details are described in this preprint: COVID-CT-Dataset: A CT Scan Dataset about COVID-19

• 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: [email protected]
• Contact: junqiangChen
• WeChat Number: 1207173174
• WeChat Public number: 最新医学影像技术