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).
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
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you can download dataset from here link
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The dataset details are described in this preprint: COVID-CT-Dataset: A CT Scan Dataset about COVID-19
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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} }
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
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the RestNet model
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training process:run the ResNet2d_train.py
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predict process:run the ResNet2d_predict.py
- the train loss and accuracy
- test data result:F1 score is 0.77,AUC area is 0.833,accuracy is 0.77.
- https://github.com/junqiangchen
- email: 1207173174@qq.com
- Contact: junqiangChen
- WeChat Number: 1207173174
- WeChat Public number: 最新医学影像技术