Skin Cancer Detection

This project aims to detect skin cancer from images using a deep learning model. The project includes training the model, making predictions, and evaluating the model's performance.

Project Structure

• models/: Directory containing the trained model.
• src/: Directory containing the training script.
• app.py: Streamlit application for skin cancer detection.
• main.py: Script to train the model.
• predict.py: Script to make predictions and evaluate the model.

Setup

1. Clone the repository:

   git clone <repository-url>
   cd Mini-project_code

2. Create a virtual environment:

   python -m venv venv

3. Activate the virtual environment:

   • On Windows:

     venv\Scripts\activate

   • On macOS/Linux:

     source venv/bin/activate

4. Install the dependencies:

   pip install -r requirements.txt

Training the Model

To train the model, run the main.py script:

python main.py

Making Predictions

To make predictions using the trained model, run the predict.py script:

python predict.py

Running the Streamlit App

To run the Streamlit app, use the following command:

streamlit run app.py

This will start the Streamlit server, and you can view the app in your web browser at the URL provided in the terminal output (usually http://localhost:8501).

Dependencies

• streamlit
• tensorflow
• numpy
• scikit-learn

These dependencies are listed in the requirements.txt file and can be installed using pip install -r requirements.txt.

License

This project is licensed under the MIT License