View the streamlit app on Huggingface 🤗
This project was made during the Data Scientist course of Datascientest, and uses the COVID-QU-Ex dataset available on Kaggle: https://www.kaggle.com/datasets/anasmohammedtahir/covidqu
[1] A. M. Tahir, M. E. H. Chowdhury, A. Khandakar, Y. Qiblawey, U. Khurshid, S. Kiranyaz, N. Ibtehaz, M. S. Rahman, S. Al-Madeed, S. Mahmud, M. Ezeddin, K. Hameed, and T. Hamid, “COVID-19 Infection Localization and Severity Grading from Chest X-ray Images”, Computers in Biology and Medicine, vol. 139, p. 105002, 2021, https://doi.org/10.1016/j.compbiomed.2021.105002.
[2] Anas M. Tahir, Muhammad E. H. Chowdhury, Yazan Qiblawey, Amith Khandakar, Tawsifur Rahman, Serkan Kiranyaz, Uzair Khurshid, Nabil Ibtehaz, Sakib Mahmud, and Maymouna Ezeddin, “COVID-QU-Ex .” Kaggle, 2021, https://doi.org/10.34740/kaggle/dsv/3122958.
[3] T. Rahman, A. Khandakar, Y. Qiblawey A. Tahir S. Kiranyaz, S. Abul Kashem, M. Islam, S. Al Maadeed, S. Zughaier, M. Khan, M. Chowdhury, "Exploring the Effect of Image Enhancement Techniques on COVID-19 Detection using Chest X-rays Images," Computers in Biology and Medicine, p. 104319, 2021, https://doi.org/10.1016/j.compbiomed.2021.104319.
[4] A. Degerli, M. Ahishali, M. Yamac, S. Kiranyaz, M. E. H. Chowdhury, K. Hameed, T. Hamid, R. Mazhar, and M. Gabbouj, "Covid-19 infection map generation and detection from chest X-ray images," Health Inf Sci Syst 9, 15 (2021), https://doi.org/10.1007/s13755-021-00146-8.
[5] M. E. H. Chowdhury, T. Rahman, A. Khandakar, R. Mazhar, M. A. Kadir, Z. B. Mahbub, K. R. Islam, M. S. Khan, A. Iqbal, N. A. Emadi, M. B. I. Reaz, M. T. Islam, "Can AI Help in Screening Viral and COVID-19 Pneumonia?," IEEE Access, vol. 8, pp. 132665-132676, 2020, https://doi.org/10.1109/ACCESS.2020.3010287.
Team:
- Thomas Baret linkedin github
- Nicolas Bouzinbi linkedin github
- Florent Daydé linkedin github
- Nicolas Fenassile linkedin github
supervised by: Gaël Penessot
How to deploy the streamlit app on Huggingface:
- Create a new space on Huggingface and clone the repository
- Push the content of the
src/streamlitdirectory - Add the model's weights file to the
modelsfolder - Store the model weights in Git LFS by adding the following line to the
.gitattributesfile:
*.h5 filter=lfs diff=lfs merge=lfs -text - Push to Huggingface
- Do not modify or delete the
REAMDE.mdfile created by Huggingface during the initialization on the space.
├── LICENSE
├── README.md <- The top-level README for developers using this project.
├── data <- Should be in your computer but not on Github (only in .gitignore)
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
│
├── demo <- Samples from the dataset for demonstration in streamlit
│
├── models <- Trained and serialized models, model predictions, or model summaries, not on Github for size reasons
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ the creator's name, and a short `-` delimited description, e.g.
│ `1.0-alban-data-exploration`.
│
│
├── reports <- The final report made during this project (PDF)
│
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
├── src <- Source code for use in this project.
│ ├── __init__.py <- Makes src a Python module
│ │
│ ├── features <- Scripts to turn raw data into features for modeling
│ │ └── build_features.py
│ │
│ ├── models <- Scripts to train models and then use trained models to make
│ │ │ predictions
│ │ ├── predict_model.py
│ │ └── train_model.py
│ │
│ │── streamlit <- Scripts for the Streamlit app
│ │
│ ├── visualization <- Scripts to create exploratory and results oriented visualizations
│ │ └── visualize.py
Project based on the cookiecutter data science project template. #cookiecutterdatascience