Run the following code to config the environment.
$ pip install -r config.txtPlease download data from here (https://www.kaggle.com/datasets/farjanakabirsamanta/skin-cancer-dataset/code) It will be 2.7G. Unzip the data to Vision-Mamba-Skin-Cancer-Classifier-main. Before do the next step, run the following code to check all code and data are at the right position.
$ ls
Mode LastWriteTime Length Name
---- ------------- ------ ----
d----- 2024/6/20 14:58 Skin Cancer
-a---- 2024/6/20 16:43 91 config.txt
-a---- 2024/6/20 15:04 1218 data_pre_process.py
-a---- 2024/6/20 14:58 563277 HAM10000_metadata.csv
-a---- 2024/6/20 15:39 2197 Loader.py
-a---- 2024/6/20 12:59 11107 Mamba_v2.py
-a---- 2024/6/20 12:59 15984 Mamba_v4.py
-a---- 2024/6/20 13:03 3663 Model.py
-a---- 2024/5/31 22:38 4503 pscan.py
-a---- 2024/6/20 21:02 5193 Test.py
-a---- 2024/6/20 20:33 7503 Train.py
Run the following code to classify all images and devide training and testing set in a ratio of 8: 2.
$ python -u data_pre_process.py
Pre processing done!
Run the follwing code to get the help.
$ python Train.py -h
Train a Mamba-based classifier to classify skin cancer diseases.
Usage:
Train.py <d_model> <state_size> <seq_len> <batch_size> <is_dropout>
<num_cls> <patch_size> <emb_out_features> <num_blocks> <epochs> <version> <cls_1> <cls_2>
Train.py (-h | --help)
General options:
-h --help Show this screen.
Arguments:
<d_model> The dimension of Mamba model.
<state_size> The parameter 'n' in Mamba paper.
<seq_len> The sequence length of Mamba model.
<batch_size> The batch size for training.
<is_dropout> Whether to use dropout or not in training. e.g. 0: False, 1: True.
<num_cls> The number of class the model is to classify.
<patch_size> The patch size for embedding.
<emb_out_features> The dimension for the output of embedding.
<num_blocks> The number of vision mamba block in model.
<epochs> Number of epochs.
<version> The version of model. e.g. v4
<cls_1> The first class from ['akiec', 'bcc', 'bkl', 'df', 'mel', 'nv', 'vasc'].
<cls_2> The second class from ['akiec', 'bcc', 'bkl', 'df', 'mel', 'nv', 'vasc'].
Run the following code to train the model, the following is to train a model to classify mel and nv.
$ python -u Train.py 192 16 258 5 1 2 16 192 8 100 v4 mel nv
start training...
...
training finished...
After each 10 epochs, programm will save the parameter of this model in .pth form, where you can find under Vision-Mamba-Skin-Cancer-Classifier-main.
Run the following code to get the help.
$ python Test.py -h
Evaluate the Mamba-based classifier to classify skin cancer diseases.
Usage:
Test.py <d_model> <state_size> <seq_len> <batch_size> <is_dropout>
<num_cls> <patch_size> <emb_out_features> <num_blocks> <epochs> <version> <cls_1> <cls_2>
Test.py (-h | --help)
General options:
-h --help Show this screen.
Arguments:
<d_model> The dimension of Mamba model.
<state_size> The parameter 'n' in Mamba paper.
<seq_len> The sequence length of Mamba model.
<batch_size> The batch size for training.
<is_dropout> Whether to use dropout or not in training. e.g. 0: False, 1: True.
<num_cls> The number of class the model is to classify.
<patch_size> The patch size for embedding.
<emb_out_features> The dimension for the output of embedding.
<num_blocks> The number of vision mamba block in model.
<epochs> Number of epochs.
<version> The version of model. e.g. v4
<cls_1> The first class from ['akiec', 'bcc', 'bkl', 'df', 'mel', 'nv', 'vasc'].
<cls_2> The second class from ['akiec', 'bcc', 'bkl', 'df', 'mel', 'nv', 'vasc'].
Run the folloing code to evaluate this model on testing set. (Notice: Parameters need to be the same as during training!!!)
$ python -u Test.py 192 16 258 5 0 2 16 192 8 100 v4 mel nv
Start testing...
precision recall f1-score support
0.0 1.00 0.93 0.96 236
1.0 0.99 1.00 0.99 1324
accuracy 0.99 1560
macro avg 0.99 0.97 0.98 1560
weighted avg 0.99 0.99 0.99 1560
Test complete...