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}
}