To read Motivation and Goal behind this research go to this Github Repository.
dataset
├── test
│ ├── covid19 [32 entries]
│ ├── normal [25 entries]
│ └── pneumonia [20 entries]
├── train
│ ├── covid19 [219 entries]
│ ├── normal [210 entries]
│ └── pneumonia [210 entries]
└── validate
├── covid19 [32 entries]
├── normal [20 entries]
└── pneumonia [20 entries]
Soon more data will be added
It is a custom model designed using Residual Blocks. Learn more about Residual block
- New model based on Densenet moudule comming soon.
- This model was trained on very small sets of images, therfore the model is not generalized enough to use.
$python3 detect.py --help
usage: detect.py [-h] -m MODEL -i IMAGE
optional arguments:
-h, --help show this help message and exit
-m MODEL, --model MODEL
path to model
-i IMAGE, --image IMAGE
path to input image
Example
python3 detect.py -m model/trained_model/covid-19_large.h5 -i model/samples/normal.jpeg
After the model was trained it was compressed into tflite model. After which it was deployed on flutter application.
model has been deplyed on a WebApp
This repository will be updating as the dataset will grow more and more. Any contribution would be appreciated.
more detailed documentation comming soon
Paper availabe here
@article{cohen2020covid,
title={COVID-19 image data collection},
author={Joseph Paul Cohen and Paul Morrison and Lan Dao},
journal={arXiv 2003.11597},
url={https://github.com/ieee8023/covid-chestxray-dataset},
year={2020}
}