python prepare_datasets_DRIVE.py
to generate hdf5 file of training datacd src
python retinaNN_training.py
to trainpython retinaNN_predict.py
to testThe results reported in the ./test
folder are referred to the trained model which reported the minimum validation loss. The ./test
folder includes:
test_model.png
schematic representation of the neural networktest_architecture.json
description of the model in json formattest_best_weights.h5
weights of the model which reported the minimum validation loss, as HDF5 filetest_last_weights.h5
weights of the model at last epoch (150th), as HDF5 filetest_configuration.txt
configuration of the parameters of the experimentperformances.txt
summary of the test results, including the confusion matrixPrecision_recall.png
the precision-recall plot and the corresponding Area Under the Curve (AUC)ROC.png
the Receiver Operating Characteristic (ROC) curve and the corresponding AUCall_*.png
the 20 images of the pre-processed originals, ground truth and predictions relative to the DRIVE testing datasetsample_input_*.png
sample of 40 patches of the pre-processed original training images and the corresponding ground truthtest_Original_GroundTruth_Prediction*.png
from top to bottom, the original pre-processed image, the ground truth and the prediction. In the predicted image, each pixel shows the vessel predicted probability, no threshold is applied.The following table compares this method to other recent techniques, which have published their performance in terms of Area Under the ROC curve (AUC ROC) on the DRIVE dataset.
Method | AUC ROC on DRIVE |
---|---|
Soares et al [1] | .9614 |
Azzopardi et al. [2] | .9614 |
Osareh et al [3] | .9650 |
Roychowdhury et al. [4] | .9670 |
Fraz et al. [5] | .9747 |
Qiaoliang et al. [6] | .9738 |
Melinscak et al. [7] | .9749 |
Liskowski et al.^ [8] | .9790 |
orobix | .9790 |
this method | .9794 |