Enhancing KL Severity Grading with Focal Loss Optimization and Interpretability through Grad CAM Analysis
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├── dataset
│ ├── train
│ │ ├── 0
│ │ ├── 1
│ │ ├── 2
│ │ ├── 3
│ │ └── 4
│ ├── val
│ │ ├── 0
│ │ ├── 1
│ │ ├── 2
│ │ ├── 3
│ │ └── 4
│ └── test
│ ├── 0
│ ├── 1
│ ├── 2
│ ├── 3
│ └── 4
├── models
├── custom_densenets.py
├── custom_resnets.py
├── Grad-CAM.ipynb
├── hyperval.py
├── README.md
├── requirements.txt
├── se_nets.py
├── test.ipynb
├── train.py
└── utils.py
Dataset can be downloaded from here.
Example statement:
python3 train.py -m resnet -d dataset -b 32 -l ce -o test -e 100 --learning_rate 1e-3
Make sure your path to the dataset is correct. Other parameters can be changed. Do refer to the parser arguments for the same.
To change any of the parameters, go to the notebook and change the parameters in the parser statement
Example statement:
python3 hyperval.py -m resnet -o test -e 100 -n 100 -s study_name
Other parameters can be changed. Do refer to the parser arguments for the same.
Model weights can downloaded from here.
| Architecture | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|
| SE-ResNet18 (CE loss) | 68.12 | 63.67 | 68.12 | 64.69 |
| SE-DenseNet (CE loss) | 69.14 | 67.21 | 69.14 | 67.85 |
| SE-ResNet18 (FL) | 67.75 | 68.56 | 67.75 | 67.83 |
| SE-DenseNet (FL) | 68.05 | 70.68 | 68.05 | 68.78 |
| SE-ResNet18 (tuned FL) | 69.38 | 67.59 | 69.38 | 67.75 |