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Getting help

If you need assistance with the Skin Cancer Detection AI application, there are several ways to get support:

GitHub Issues

Report bugs, request features, or ask technical questions

Documentation

Browse the complete documentation for setup and usage guides

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Before requesting support

Please check the following before opening an issue:
  1. Review the FAQ for common questions and solutions
  2. Search existing issues to see if your problem has been reported
  3. Ensure you’re using a supported browser
  4. Check your browser’s console for error messages

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Reporting issues

When reporting a bug or issue, please include:
1

Clear description

Describe what happened and what you expected to happen
2

Steps to reproduce

Provide detailed steps to reproduce the issue:
  • What actions did you take?
  • What image format did you use?
  • When did the error occur?
3

Environment details

Include information about your setup:
  • Browser name and version
  • Operating system
  • Any error messages from the console
4

Screenshots

If applicable, add screenshots or screen recordings showing the issue
Privacy reminder: Never include actual medical images or personal health information in issue reports. Use sample or test images only.

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Issue template

**Describe the bug**
A clear description of what the bug is.

**To reproduce**
1. Go to '...'
2. Click on '...'
3. Upload image '...'
4. See error

**Expected behavior**
What you expected to happen.

**Environment**
- Browser: [e.g., Chrome 120]
- OS: [e.g., Windows 11, macOS 14]
- Error message: [paste from console]

**Screenshots**
Add screenshots if applicable.

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Contributing

Contributions to the Skin Cancer Detection AI project are welcome! Here’s how you can help:

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Types of contributions

Help improve the application by:
  • Fixing bugs
  • Adding new features
  • Improving performance
  • Enhancing error handling
  • Optimizing model loading
  • Improving the user interface
The main codebase is in JavaScript and uses TensorFlow.js for browser-based inference.
Enhance the machine learning model by:
  • Improving training data preprocessing
  • Experimenting with different architectures
  • Increasing model accuracy
  • Reducing model size for faster loading
  • Adding data augmentation techniques
The model is trained using Python with Keras and converted to TensorFlow.js format.
Help others understand and use the project by:
  • Writing tutorials or guides
  • Improving existing documentation
  • Adding code comments
  • Creating examples
  • Translating documentation
Improve project quality by:
  • Testing the application on different browsers
  • Reporting bugs with detailed reproduction steps
  • Validating model predictions
  • Testing edge cases
  • Providing feedback on user experience

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Contribution workflow

1

Fork the repository

Create your own fork of the SkinCancerDetectionAI repository
2

Clone your fork

git clone https://github.com/YOUR_USERNAME/SkinCancerDetectionAI.git
cd SkinCancerDetectionAI
3

Create a branch

git checkout -b feature/your-feature-name
Use descriptive branch names like:
  • fix/image-loading-error
  • feature/add-export-results
  • docs/update-setup-guide
4

Make your changes

  • Write clean, readable code
  • Follow existing code style
  • Test your changes thoroughly
  • Add comments for complex logic
5

Commit your changes

git add .
git commit -m "Add descriptive commit message"
Write clear commit messages that explain what and why, not just what.
6

Push to your fork

git push origin feature/your-feature-name
7

Open a pull request

Go to the original repository and open a pull request from your branch.In your PR description:
  • Explain what changes you made
  • Describe why these changes are needed
  • Reference any related issues
  • Include screenshots for UI changes

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Code guidelines

// Use meaningful variable names
const predictedClass = prediction.indexOf(Math.max(...prediction));

// Add error handling
try {
    // Your code here
} catch (error) {
    console.error('Detailed error message:', error);
    alert('User-friendly error message');
}

// Comment complex logic
// Resize image to model input size (75x100) and normalize
let tensorImg = tf.browser.fromPixels(imgtag)
    .resizeNearestNeighbor([75, 100])
    .toFloat()
    .expandDims();
Important: All contributions must maintain the educational and research-only nature of this project. Do not add features that could encourage medical self-diagnosis or clinical use.

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Project repository

The complete source code is available on GitHub:

SkinCancerDetectionAI

View the source code, star the repository, and track project updates

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Repository structure

SkinCancerDetectionAI/
├── skin_cancer_diagnosis_script.js    # Main application logic
├── model.json                          # TensorFlow.js model architecture
├── cnn_model/                          # Model weight files
├── *.bin                               # CNN weights from Python training
└── README.md                           # Project overview

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Community guidelines

When participating in the project:
  • Be respectful and professional
  • Provide constructive feedback
  • Help others learn and grow
  • Focus on technical merit
  • Remember the educational purpose of this project
  • Never share real medical images or patient information

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License and usage

Please check the repository for the most current license information and usage terms.
This project is intended for:
  • Educational purposes
  • Research and experimentation
  • Learning about medical AI and computer vision
  • Understanding CNN architectures
  • Exploring TensorFlow.js capabilities
Not intended for:
  • Medical diagnosis
  • Clinical applications
  • Healthcare decision-making
  • Commercial medical products
Have questions about contributing? Open a discussion on GitHub and the community will help you get started.

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