ET-NET_Covid-Detection
Based on our paper "ET-NET: An Ensemble of Transfer Learning Models for Prediction of COVID-19 Infection through Chest CT-scan Images" published in Springer- Multimedia Tools and Applications.
Required Dependencies
To install the dependencies, run the following using the command prompt:
pip install -r requirements.txt
Running the code on the COVID data
Download the dataset from Kaggle and split it into 5-fold cross-validation train and validation sets.
Required Directory Structure:
+-- data
| +-- .
| +-- train
| +-- val
+-- utils
| +-- .
| +-- utils_cnn
| +-- utils_ensemble.py
+-- main.py
To run the ensemble model on the base learners run the following:
python main.py --root "path/"
Available arguments:
--epochs: Number of epochs of training. Default = 100--batch_size: Batch Size. Default = 4--num_workers: Number of Worker processes. Default = 2--learning_rate: Learning Rate. Default = 0.001--momentum: Momentum value. Default = 0.99
Citation
If this repository helps you in any way, please consider citing our paper:
Kundu, Rohit, et al. "ET-NET: an ensemble of transfer learning models for prediction of COVID-19 infection through chest CT-scan images." Multimedia Tools and Applications (2021): 1-20.
Bibtex:
@article{kundu2021net,
title={ET-NET: an ensemble of transfer learning models for prediction of COVID-19 infection through chest CT-scan images},
author={Kundu, Rohit and Singh, Pawan Kumar and Ferrara, Massimiliano and Ahmadian, Ali and Sarkar, Ram},
journal={Multimedia Tools and Applications},
pages={1--20},
year={2021},
publisher={Springer}
}