Cervical Cytology Classification Using PCA & GWO Enhanced Deep Features Selection

Official Python Implementation of the paper titled "Cervical Cytology Classification Using PCA & GWO Enhanced Deep Features Selection" accepted for publication in special issue "AI and Deep Learning Trends in Healthcare" of SpringerNature Computer Science.

Requirements

To install the required dependencies run the following using the Command Prompt:

pip install -r requirements.txt

Implementing the code for Cervical Cytology data

Similarly the script can be modified for extracting features from other models.

Structure the directory as follows:

.
+-- data
|   +-- .
|   +-- train
|   +-- val
+-- extract_features.py
+-- fitnessFUNs.py
+-- GWO.py
+-- main.py
+-- resnet50.csv
+-- selector.py
+-- solution.py
+-- transfer_functions_benchmark.py

To extract ResNet-50 features run the following script:

python extract_features.py

Run the following code for the feature set optimization:

python main.py --num_csv 2

Set num_csv to the number of features csv files you have. You will be asked to enter the names of the csv files upon executing the above code. Execute python main.py -h to get the details of all the available arguments.

Citation

If this repository helps you in your research in any way, please cite our paper:

@article{basak2021cervical,
      title={Cervical Cytology Classification Using PCA & GWO Enhanced Deep Features Selection}, 
      author={Hritam Basak and Rohit Kundu and Sukanta Chakraborty and Nibaran Das},
      year={2021},
      eprint={2106.04919},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}