"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.

1. SIPaKMeD SCI Pap Smear Images
2. Mendeley LBC

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