COVID-19 Detection from X-rays

Introduction

Prediction of Covid-19 positive from X-rays. The images in the Dataset used are Denoised with Convolutional Variational AutoEncoder (hyperparameterized for Covid-19 Dataset) and a Convolutional Neural Network is used for classification with softmax entropy loss.

Can X-rays be used for Covid-19 Detection?

This work supports it: https://pubs.rsna.org/doi/10.1148/radiol.2020200432 and https://pubs.rsna.org/doi/10.1148/radiol.2020200642

And this recommends not using it: https://www.acr.org/Advocacy-and-Economics/ACR-Position-Statements/Recommendations-for-Chest-Radiography-and-CT-for-Suspected-COVID19-Infection

Note : This model doesn't claim any diagnostic performance. This is merely an implementation of neural networks for classification.

Read about the problems in deep learning for COVID-19 Detection in https://arxiv.org/abs/2004.12823 and https://arxiv.org/abs/2004.05405

Dataset

The dataset used is publicly available at https://github.com/ieee8023/covid-chestxray-dataset. The dataset can be used for research purposes and belongs to the owner of the repository.

The dataset contains X-rays of various diseases, but the X-rays of only Covid-19 Positive are extracted. The healthy X-rays are extracted from https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia. The dataset ratio of positive:healthy were kept to 50%-50%

Note : Read the earlier analysis done with this dataset in the above linked papers.

Results

The model achieves 0.968 accuracy, 0.9877 AUC scores on train set and 0.901 accuruacy , 0.92441 AUC scores on test set after 20 epochs of training on Google Colab with GPU for ~30 mins.

The model however performs really poorly on complex structures such as in this research. A Sequential single input and single output architecture with increasing filters outperforms any other complex structures.

Executing

Extracting data

from src.data.extract import class_wise, train_test_wise

if __name__ == "__main__":

    class_wise('src/dataset/input', 'src/dataset/classwise')
    train_test_wise("src/dataset/classwise/", "src/dataset/traintest/" , 0.5, True)

Training model

python -m src.model.cnn

Using GUI

python -m src.gui.main