README.md

Mean and standard deviation based ensemble network for cervical cancer detection

"MSENet: Mean and standard deviation based ensemble network for cervical cancer detection" published in Engineering Applications of Artificial Intelligence, Elsevier (Aug 2023)

@article{pramanik2023msenet,
  title={MSENet: Mean and standard deviation based ensemble network for cervical cancer detection},
  author={Pramanik, Rishav and Banerjee, Bihan and Sarkar, Ram},
  journal={Engineering Applications of Artificial Intelligence},
  volume={123},
  pages={106336},
  year={2023},
  publisher={Elsevier}
}

MSENet: Mean and standard deviation based ensemble network for cervical cancer detection

Find the original paper Here.

Datasets Links

1. SIPaKMeD SCI Pap Smear Images
2. Mendeley LBC

Instructions to run the code

Required directory structure: (Note: train and test contains subfolders representing classes in the dataset.)

+-- data
|   +-- train
|   |   +--class A
|   |   +--class B
|   |   ...
|   +-- test
|   |   +--class A
|   |   +--class B
|   |   ...
+-- main.py

1. Download the repository and install the required packages:

pip3 install -r requirements.txt

2. The main file is sufficient to run the experiments. Then, run the code using linux terminal as follows:

python3 main.py --data_directory "data"

Available arguments:

• --num_epochs: Number of epochs of training. Default = 75
• --learning_rate: Learning Rate. Default = 0.0001
• --batch_size: Batch Size. Default = 32
• --path: Data Path. Default= './'
• --kfold: K-Fold, to perform K fold cross validation. Default= 5

3. Please don't forget to edit the above parameters before you start