1- Download the Lung Segmentation dataset from Kaggle link and extract it.
2- Run Prepare_data.py for data preperation, train/test seperation and generating new masks around the lung tissues.
3- Run train_lung.py for training BCDU-Net model using trainng and validation sets (20 percent of the training set). The model will be train for 50 epochs and it will save the best weights for the valiation set. You can train either BCDU-net model with 1 or 3 densly connected convolutions.
4- For performance calculation and producing segmentation result, run evaluate_performance.py. It will represent performance measures and will saves related figures and results.
| Methods | Year | F1-scores | Sensivity | Specificaty | Accuracy | AUC | JS |
|---|---|---|---|---|---|---|---|
| Ronneberger and etc. all U-net | 2015 | 0.9658 | 0.9696 | 0.9872 | 0.9872 | 0.9784 | 0.9858 |
| Alom et. all Recurrent Residual U-net | 2018 | 0.9638 | 0.9734 | 0.9866 | 0.9836 | 0.9800 | 0.9836 |
| Alom et. all R2U-Net | 2018 | 0.9832 | 0.9944 | 0.9832 | 0.9918 | 0.9889 | 0.9918 |
| Azad et. all Proposed BCDU-Net | 2019 | 0.9904 | 0.9910 | 0.9982 | 0.9972 | 0.9946 | 0.9972 |
| QUNet | - | 0.9908 | 0.9912 | 0.9985 | 0.9974 | 0.9915 | 0.9974 |
Applications of paper Quadratic Autoencoder (Q-AE) for Low-Dose CT Denoising (https://ieeexplore.ieee.org/document/8946589)