Surveillance of neonatal jaundice using sternal skin images via deep learning
This code uses convolutional neural network to estimate neonatal jaundice of sternal skin images.
imgs: 12 skin images of 4 newborns with labels ('imgs/labels.csv'), and the prediction results of these images ('imgs/results.csv').
regression.py: The train code.
test.py: The test code.
utils.py: Some functions.
The proposed network requires a computer with GPU to run the convolutional neural network. For optimal performance, we recommend a computer with the following specs:
RAM: 16+ GB
GPU: NVIDIA 1080Ti
The code has been tested on Linux operating systems.
Users should install the following python packages prior to run the code:
python3.5 tensorflow-gpu keras pandas numpy opencv
We have provided the trained weight of the proposed network in https://drive.google.com/file/d/1Q_D_Oh59XiZEsjc0CqZMs8KSm4oyxGy_/view?usp=sharing, you can download the weight and run 'test.py' directly to estimate the bilirubin value of images in folder 'imgs'. We also provide the prediction results of 'test.py' for these images in 'imgs/results.csv'.
The 'regression.py' is used to train the proposed network, but the training data is too large to provide, so 'regression.py' may not run in your computer.