Skin Lesion Classification App is a Flask web application that allows users to select different models trained using TensorFlow to classify images of skin lesions. Users can upload their own images and choose a model to generate a report classifying the image. The app also includes tabs for Exploratory Data Analysis (EDA) and model comparison to provide users with more information about the available ML models.
The Skin Lesion Classification App is designed to assist users in classifying skin lesions through machine learning models. It provides an interactive platform to upload images, select a model, and generate classification reports.
- Upload images for classification
- Select from various TensorFlow models
- Generate classification reports
- Exploratory Data Analysis (EDA) tab
- Model comparison tab
- Python 3.11
- Flask
- TensorFlow
- Other dependencies (specified in requirements.txt)
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Clone the repository:
git clone https://github.com/maxcollins457/SkinCancerApp.git cd SkinCancerApp
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Install dependencies
pip install -r requirements.txt
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Run the flask app using
python main.py
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Open your browser and navigate to http://localhost:5000.
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Explore the different tabs for image classification, EDA, and model comparison.
This is a brief overview of the project structure.
SkinCancerApp/
│
├── app/
│ ├── data/
│ │ └── CancerData.csv
│ ├── networks/
│ │ └── h5 keras models
│ ├── static/
│ │ ├── css/
│ │ ├── img/
│ │ └── js/
│ ├── templates/
│ │ └── html templates
│ └── views/
│ └── flask views
│ ├── __init__.py
│ ├── models.py
│
├── COLAB NOTEBOOKS/
│ └── ipynb notebooks
│
├── requirements.txt
├── README.md
└── main.py
- data: Processed metadata from the dataset.
- networks: Stores trained machine learning models and related files.
- static: Images, CSS and JS for aesthetic of the flask app.
- templates: HTML templates for the flask app.
- COLAB NOTEBOOKS: Jupyter notebooks for exploratory data analysis and model training.
- requirements.txt: Lists project dependencies.
- README.md: Project documentation.