COVID-Detection-Gompertz-Function-Ensemble

This is the official implementation of the paper titled "Fuzzy Rank-based Fusion of CNN Models using Gompertz Function for Screening COVID-19 CT-Scans" published in "Nature- Scientific Reports".

Requirements

To install the dependencies, run the following using the command prompt:

pip install -r requirements.txt

Running the code on the COVID data

In this repository we take the example of the SARS-COV-2 dataset [1] used in the paper to run the ensemble codes.

Download the dataset from Kaggle and split it into train and validation sets in 80-20 ratio.

Required Directory Structure:

+-- data
|   +-- .
|   +-- train
|   +-- val
+-- sar-cov-2_csv
|   +-- .
|   +-- inception.csv
|   +-- vgg11.csv
|   +-- wideresnet50-2.csv
+-- main.py
+-- probability_extraction
+-- utils_ensemble.py

To extract the probabilities on the validation set using the different models run probability_extraction.py and save the files in a folder. As an example the probabilities extracted on the SARS-COV-2 dataset has been saved in the folder named sars-cov-2_csv/.

Next, to run the ensemble model on the base learners run the following:

python main.py --data_directory "sars-cov-2_csv/"

References:

[1] Soares, E., Angelov, P., Biaso, S., Froes, M. H. & Abe, D. K. Sars-cov-2 ct-scan dataset: A large dataset of real patients ct scans for sars-cov-2 identification. medRxiv (2020).

Citation

If you find this repository useful, please cite our work as follows:

@article{kundu2021fuzzy,
  title={Fuzzy rank-based fusion of CNN models using Gompertz function for screening COVID-19 CT-scans},
  author={Kundu, Rohit and Basak, Hritam and Singh, Pawan Kumar and Ahmadian, Ali and Ferrara, Massimiliano and Sarkar, Ram},
  journal={Scientific Reports},
  volume={11},
  number={1},
  pages={1--12},
  year={2021},
  publisher={Nature Publishing Group}
}