This notebook includes implementation of several machine learning techniques in order to solve bone fracture classification problem by determining whether an X-ray study is normal or abnormal.

Dataset that used in this project is called MURA which is a large dataset of bone X-rays.

Classification Methods:

• KNN (sklearn).
• ANNs (tf.keras), vanilla and pretrained models.

ps: All the libraries can be downloaded by pip install -r requirements.txt

absl-py==0.13.0
astunparse==1.6.3
cachetools==4.2.2
certifi==2021.5.30
chardet==4.0.0
click==8.0.1
Flask==2.0.1
flatbuffers==1.12
gast==0.4.0
google-auth==1.32.1
google-auth-oauthlib==0.4.4
google-pasta==0.2.0
grpcio==1.34.1
h5py==3.1.0
idna==2.10
itsdangerous==2.0.1
Jinja2==3.0.1
Keras==2.4.3
keras-nightly==2.5.0.dev2021032900
Keras-Preprocessing==1.1.2
Markdown==3.3.4
MarkupSafe==2.0.1
numpy==1.19.5
oauthlib==3.1.1
opt-einsum==3.3.0
Pillow==8.3.0
protobuf==3.17.3
pyasn1==0.4.8
pyasn1-modules==0.2.8
PyYAML==5.4.1
requests==2.25.1
requests-oauthlib==1.3.0
rsa==4.7.2
scipy==1.7.0
six==1.15.0
tensorboard==2.5.0
tensorboard-data-server==0.6.1
tensorboard-plugin-wit==1.8.0
tensorflow==2.5.0
tensorflow-estimator==2.5.0
termcolor==1.1.0
typing-extensions==3.7.4.3
urllib3==1.26.6
Werkzeug==2.0.1
wrapt==1.12.1

├── app
│   ├── static
│   │   ├── css
│   │   ├── js
│   │   └── uploads
│   └── templates
├── notebooks
├── results
│   ├── figures
│   ├── logs
│   └── models
├── tests
└── venv

1. Clone repo to your local: $ git clone https://github.com/erd3muysal/bone_fracture_classification.
2. Go to project directory and create a virtual envrironment.
   • $ python -m venv env
   • $ source env/bin/activate
3. Install dependinces by typing pip install -r requirements.txt.
4. Train the model. You have two options for training process.
   • Notebook
     • You can use Google Colab for start training using notebook. or
   • Scripts
     • To check optional arguments type $ python3 __init__.py --help
     • To start training type $ python3 __init__.py This process will save the best model as a .h5 file under results/models directory.
5. Set Flask environment variables.
   • export FLASK_APP=app

$ flask run

R. Erdem Uysal

• https://www.linkedin.com/in/erdemuysal13/
• https://github.com/erd3muysal