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In this research we concentrate on the different algorithm of machine learning and deep learning. Computed tomography (CT) scan images used for the research .for the implementation of the liver tumor segmentation we used convolution neural network algorithm like U-Net and V-Net. We processed our experiment on liver tumor segmentation (LiTS) data…
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Graphical_n_meta_utils.py
README.md Update README.md Jul 18, 2019
Train_bmp_conversion.py
Train_n_Predict.py Add files via upload Jul 18, 2019
layers.py Add files via upload Jul 18, 2019
model.py
prediction.py Add files via upload Jul 18, 2019
test_npy_conversion.py Add files via upload Jul 18, 2019
test_preprocessing.py
train_X.csv
train_Y.csv Add files via upload Jul 18, 2019
training.py Add files via upload Jul 18, 2019
utils.py

README.md

LiTS

This code is the solution to the Liver Tumor Segmentation Challenge from www.codalab.org To run this code, the train images and masks should be converted to .bmp files and can be done through Train_bmp_conversion.py and these files can be listed into csv using utils.py training.py can be used for training the model(VNET) in this case and prediction can be done by prediction.py i.e, converting the test data into .bmp or npy files using the test_npy_conversion.py file.

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