Here is an implementation of CovidCTNet model for the prediction of COVID-19 cases based on CT images.
For more detail about the project please use this link to access the paper.
These codes are implemented on Google Colab and for Neural Networks models we use Keras. Any library you need for this
project is provided inside the codes.
You can find trained weights in Model_weight folder.
These codes prepared for dicom CT images. Please clone the repository and copy all .dcm files of a patient in a folder and copy all folders of patients
in related folder in /Data/DCM/... folder.
Before training please provide Dicom images. If you want to use trained model, please download weights and add them to the "Model_weight" folder. Following steps can be helpful for your training:
- Run all cells of
/Code/preprocessing/preprocessing-step-1.ipynbin sequential order. - Run all cells of
/Code/preprocessing/preprocessing-step-2.ipynbinsequential order. - Run all cells of
/Code/training and testing/Training-CovidCTNet.ipynbin sequential order.
- If you want to train the model from scratch, don't use any weight loading in step 3.
- To train the 3D CNN model and classify if the CT image of the new patient is COVID-19, CAP or Control; you can either initialize weights of our trained model in the third link at the table, or train from the scratch on your own.
For testing the model on your dataset, please follow this list:
- Run all cells of
/Code/preprocessing/preprocessing-step-1.ipynbin sequential order. - Run all cells of
/Code/preprocessing/preprocessing-step-2.ipynbin sequential order. - Run all cells of
/Code/training and testing/Testing-CovidCTNet.ipynbin sequential order.
** Please make sure you have enough space on your drive. Step 1 and 2 of preprocessing will convert your dcm file to numpy files to use and
all subfolders in preprocessed folder will be occupied with referred numpy files. If you have any problem with the space in your drive you can increase spacing
form [1,1,1] in preprocessing to larger numbers.