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Skin cancer misdiagnosis by dermatologists is not uncommon. 25-33% of the skin cancers are incorrectly diagnosed as eczema or another less serious disease. The overall goal is to design and implement an application that can effectively classify whether a patient developed skin cancer based on photographs of skin lesions on dermatologic slides.

License: Other

Makefile 0.04% Python 0.11% Jupyter Notebook 83.09% PureBasic 16.76%
dermatology histopathology machine-learning

dsei210-final-project--skinjob's Introduction

SkinJob

Final Project for DSE I2100

Instruction

  1. Please download the data_helper.py from src/data folder
  2. Please download model_build_hepler.py from src/models
  3. Please download CNN_main.ipynp from src folder
  4. Upload the data_helper.py and model_build_hepler.py to /content directory in colab
  5. Update the Path input for model.save() in CNN_main jupyter notebook
  6. Run the CNN_main jupyter notebook
  7. A saved model will be in the path given in step 3

Project Organization

├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── 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
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── data           <- Scripts to download or generate data
│   │   └── make_dataset.py
│   │
│   ├── 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
│   │
│   └── visualization  <- Scripts to create exploratory and results oriented visualizations
│       └── visualize.py
│
└── tox.ini            <- tox file with settings for running tox; see tox.readthedocs.io

Project based on the cookiecutter data science project template. #cookiecutterdatascience

dsei210-final-project--skinjob's People

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albert6051 avatar huihuangliu001 avatar

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