This project is my attempt to automate the process of malaria diagnosis by building an image classifier based on a residual neural network. Properly trained, it may significantly improve the quality of the malaria diagnosis and automate the process thus freeing the humans for other tasks.
An image classifier is build with ResNet models using fast.ai library.
├── LICENSE ├── README.md <- The top-level README for developers using this project. ├── data ├── docs <- A default Sphinx project; see sphinx-doc.org for details ├── models <- Trained and serialized models, model predictions, or model summaries ├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering), │ the creator's initials, and a short `-` delimited description, e.g. │ `1.0-jqp-initial-data-exploration`. │ ├── references <- Data dictionaries, manuals, and all other explanatory materials. ├── reports <- Generated analysis as HTML, PDF, LaTeX, etc. │ └── figures <- Generated graphics and figures to be used in reporting ├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g. │ generated with `pip freeze > requirements.txt` ├── setup.py <- makes project pip installable (pip install -e .) so src can be imported
Project based on the cookiecutter data science project template. #cookiecutterdatascience