talhaanwarch / Corona_Virus

Diagnosis of corona virus using Chest X-ray and CT-scan through deep learning

Corona_Virus

Diagnosed using X-ray and CT-scan

Two different dataset has been tried.

• X-ray dataset
• CT-scan dataset

Results using Chest Xray dataset

There are three classes in this dataset

• covid
• normal
• pneumonia

No seprate test holdout dataset is used. Instead 5 fold cross validation technique is used. The dataset is taken from tawsifur/COVID-19-Chest-X-ray-Detection github repo. The author achieved an F1-score of 0.983, where as we got an F1-score of 0.981.

	Precision	Recall	F1-score	Accuracy
Fold 1	0.99	0.98	0.98	0.98
Fold 2	0.99	0.98	0.99	0.98
Fold 3	0.97	0.98	0.98	0.97
Fold 4	0.98	0.98	0.98	0.97
Fold 5	0.98	0.98	0.98	0.97

We achieved an F1 score of 0.98. Result can be reproduce through this notebook

In order to download data in notebook you need to provide it a kaggle API. Go to kaggle.com, click on my account and then go to API section. Click on create new API token. It will download a file , kaggle.json . upload this kaggle.json to content folder of colab. And run the whole notebook.

Result using CT-scan dataset

Dataset is taken from github.com/UCSD-AI4H/COVID-CT repo, where author achieved an F1-score of 0.85.
All CT-scan related files are in CT-scan folder
Here i study effect of learning rate on model performance.

	Plateau LR	Cyclic LR	Constant LR
Macro Average Precision	0.90	0.89	0.85
Macro Average Recall	0.89	0.86	0.82
Macro Average F1 Score	0.90	0.86	0.82
Accuracy	0.90	0.86	0.86
AUC score	0.90	0.86	0.82

Jeremy Kohn has compiled all image based diagnostic of coronavirus at one place. Do check his repo github.com/jeremykohn/rid-covid

PAPER citation

@INPROCEEDINGS{9318212,  
author={T. {Anwar} and S. {Zakir}},  
booktitle={2020 IEEE 23rd International Multitopic Conference (INMIC)},   
title={Deep learning based diagnosis of COVID-19 using chest CT-scan images},   
year={2020},  volume={},  number={},  pages={1-5},  
doi={10.1109/INMIC50486.2020.9318212}}